From 5b258682a4928491d7194bb0f24af83cddf4187c Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 00:33:51 +0500 Subject: [PATCH 01/24] Delete Base_Model_Python.py --- Base_Model_Python.py | 994 ------------------------------------------- 1 file changed, 994 deletions(-) delete mode 100644 Base_Model_Python.py diff --git a/Base_Model_Python.py b/Base_Model_Python.py deleted file mode 100644 index 9d249f2..0000000 --- a/Base_Model_Python.py +++ /dev/null @@ -1,994 +0,0 @@ -''' -Created on 24th May 2022 - -Energy Planning Scenario in Pyomo - -This script presents the mathematical optimisation formulation concerning -carbon-constrained energy planning for a specific geographical region or -district. - -Power plants are fuelled by either renewable energy sources e.g., solar, -hydropower etc or fossil-based sources e.g., coal, natural gas etc. - -This energy planning is conducted for several periods (user-specified), with -each period having its respective demand and CO2 emission limit - -The satisfaction of the CO2 load limit is via the deployment of compensatory -energy, CCS technology, alternative low-carbon fuels (replacing fossil fuels), -and negative emission technologies (NETs) - -NETs are either made up of energy-producing NETs (EP-NETs) or energy-consuming -NETs (EC-NETs). Examples of EP-NETs are biomass and biochar, while EC-NETs -are made up of enhanced weathering, direct air capture etc. - -EP-NETs and EC-NETs produce and consume energy during CO2 removal respectively - -Alternative solid and gas fuels have low CO2 intensity, with a potential of -replacing coal and natural gas in fossil-based power plants respectively - -@author: Purusothmn, Dr Michael Short - -''' -import pyomo.environ as pyo -from pyomo.opt import SolverFactory -import pandas as pd -import os -from openpyxl import load_workbook - -cwd = os.getcwd() -model = pyo.ConcreteModel() - -file_name = r'Base_User_Interface.xlsx' -model.plant = pd.read_excel(file_name, sheet_name = 'PLANT_DATA', index_col = 0, header = 32, nrows = 7).to_dict() -model.EP = pd.read_excel(file_name, sheet_name = 'ENERGY_PLANNING_DATA', index_col = 0, header = 7).to_dict() -model.fuel = pd.read_excel(file_name, sheet_name = 'FUEL_COST_DATA', index_col = 0, header = 12).to_dict() -model.REN_CI = pd.read_excel(file_name, sheet_name = 'RENEWABLE_CI_DATA', index_col = 0, header = 9).to_dict() -model.REN_COST = pd.read_excel(file_name, sheet_name = 'RENEWABLE_COST_DATA', index_col = 0, header = 9).to_dict() -model.CPX_1 = pd.read_excel(file_name, sheet_name = 'CAPEX_DATA_1', index_col = 0, header = 20).to_dict() -model.CPX_2 = pd.read_excel(file_name, sheet_name = 'CAPEX_DATA_2', index_col = 0, header = 20).to_dict() -model.SLD_CI = pd.read_excel(file_name, sheet_name = 'ALT_SOLID_CI', index_col = 0, header = 6).to_dict() -model.SLD_COST = pd.read_excel(file_name, sheet_name = 'ALT_SOLID_COST', index_col = 0, header = 6).to_dict() -model.GAS_CI = pd.read_excel(file_name, sheet_name = 'ALT_GAS_CI', index_col = 0, header = 6).to_dict() -model.GAS_COST = pd.read_excel(file_name, sheet_name = 'ALT_GAS_COST', index_col = 0, header = 6).to_dict() -model.CCS_data = pd.read_excel(file_name, sheet_name = 'CCS_DATA', index_col = 0, header = 12).to_dict() -model.NET_CI = pd.read_excel(file_name, sheet_name = 'NET_CI_DATA', index_col = 0, header = 12).to_dict() -model.NET_COST = pd.read_excel(file_name, sheet_name = 'NET_COST_DATA', index_col = 0, header = 12).to_dict() -model.TIME = pd.read_excel(file_name, sheet_name = 'TECH_IMPLEMENTATION_TIME', index_col = 0, header = 19).to_dict() - -wb = load_workbook(file_name) - -sheet_1 = wb['PLANT_DATA'] -flag = sheet_1['B30'].value - -numperiods = sheet_1['B31'].value + 1 - -sheet_2 = wb['CAPEX_DATA_1'] -AFF = sheet_2['B18'].value - -periods = list(range(1,numperiods,1)) - -def multiperiod_energy_planning(model, i): - model.S = model.plant.keys() - - #LIST OF VARIABLES - #This variable determines the deployment of energy sources in power plant s for period i - model.energy = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the CO2 intensity of energy sources in power plant s with CCS technology 1 for period i - model.CI_RET_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the CO2 intensity of energy sources in power plant s with CCS technology 2 for period i - model.CI_RET_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #Binary variable for power generation by power plant s for period i - model.A = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of CCS technology 1 in power plant s for period i - model.B = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of CCS technology 2 in power plant s for period i - model.C = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of EP-NETs technology 1 for period i - model.D = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EP-NETs technology 2 for period i - model.E = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EP-NETs technology 3 for period i - model.F = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EC-NETs technology 1 for period i - model.G = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EC-NETs technology 2 for period i - model.H = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EC-NETs technology 3 for period i - model.I = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of solar renewable energy for period i - model.J = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of hydropower renewable energy for period i - model.K = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of biomass renewable energy for period i - model.L = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of biogas renewable energy for period i - model.M = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of MSW renewable energy for period i - model.N = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of alternative solid-based fuel technology 1 in power plant s for period i - model.O = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of alternative solid-based fuel technology 2 in power plant s for period i - model.P = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of alternative gas-based fuel technology 1 in power plant s for period i - model.Q = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of alternative gas-based fuel technology 2 in power plant s for period i - model.R = pyo.Var(i, model.S, domain = pyo.Binary) - - #This variable represents the deployment of CCS technology 1 in power plant s for period i - model.CCS_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable represents the deployment of CCS technology 2 in power plant s for period i - model.CCS_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the net energy available from power plant s without CCS deployment for period i - model.net_energy = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the net energy available from power plant s with the deployment of CCS technology 1 for period i - model.net_energy_CCS_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the net energy available from power plant s with the deployment of CCS technology 2 for period i - model.net_energy_CCS_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EP_NETs technology 1 for period i - model.EP_NET_1 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EP_NETs technology 2 for period i - model.EP_NET_2 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EP_NETs technology 3 for period i - model.EP_NET_3 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EC_NETs technology 1 for period i - model.EC_NET_1 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EC_NETs technology 2 for period i - model.EC_NET_2 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EC_NETs technology 3 for period i - model.EC_NET_3 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of solar renewable energy for period i - model.REN_SOLAR = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of hydro renewable energy for period i - model.REN_HYDRO = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of biomass renewable energy for period i - model.REN_BM = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of biogas renewable energy for period i - model.REN_BG = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of MSW renewable energy for period i - model.REN_MSW = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of alternative solid-based fuel technology 1 for coal-based plant s for period i - model.solid_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of alternative solid-based fuel technology 2 for coal-based plant s for period i - model.solid_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of alternative gas-based fuel technology 1 for natural gas-based plant s for period i - model.gas_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of alternative gas-based fuel technology 2 for natural gas-based plant s for period i - model.gas_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the revised total CO2 emissions for period i - model.new_emission = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the total energy cost of power plant s for period i - model.energy_cost = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) - - #This variable determines the total energy planning cost for period i - model.sum_cost = pyo.Var(i, domain = pyo.NonNegativeReals) - - - #OBJECTIVE FUNCTION - - #For the minimum budget objective function, the total cost is minimised, subject to the satisfaction of the CO2 emission limit - #For the minimum emission objective function, the total emission is minimised, subject to the available budgetary constraint - if flag == 'min_budget': - model.obj = pyo.Objective(expr = sum(model.sum_cost[i] for i in periods), sense = pyo.minimize) - else: - model.obj = pyo.Objective(expr = sum(model.new_emission[i] for i in periods), sense = pyo.minimize) - - #CONSTRAINTS - - - #Prior to any energy planning, the total power generation from all power plants should satisfy the regional energy demand for period i - def demand(model, i, s): - return model.EP['Demand'][i] == sum(model.energy[i,s] for s in model.S) - - model.Cons_1 = pyo.Constraint(i, model.S, rule = demand) - - - #The deployment of energy source in power plant s should at least satisfy the lower bound for period i - def lower_bound_energy(model, i, s): - return model.energy[i,s] >= model.plant[s]['LB'] * model.A[i,s] - - model.Cons_2 = pyo.Constraint(i, model.S, rule = lower_bound_energy) - - - #The deployment of energy source in power plant s should at most satisfy the upper bound for period i - def upper_bound_energy(model, i, s): - return model.energy[i,s] <= model.plant[s]['UB'] * model.A[i,s] - - model.Cons_3 = pyo.Constraint(i, model.S, rule = upper_bound_energy) - - - #There should not be power generation from power plant s before its commissioning period - def plant_commission(model, i, s): - if i < model.plant[s]['ON']: - return model.energy[i,s] == 0 - else: - return model.energy[i,s] >= 0 - - model.Cons_4 = pyo.Constraint(i, model.S, rule = plant_commission) - - - #There should be power generation from power plant s after its decommissioning period - def plant_decommission(model, i, s): - if i >= model.plant[s]['OFF']: - return model.energy[i,s] == 0 - else: - return model.energy[i,s] >= 0 - - model.Cons_5 = pyo.Constraint(i, model.S, rule = plant_decommission) - - - #If power plant s is decomissioned in a period, it should remain decommissioned at later periods - #However, power plant s should be available for power generation till the period before its decommissioning period - def energy_constraint(model, i, s): - if i <= model.plant[s]['OFF'] - 2: - return model.energy[i+1,s] >= model.energy[i,s] - else: - return pyo.Constraint.Skip - - model.Cons_6 = pyo.Constraint(i, model.S, rule = energy_constraint) - - - #Calculation of carbon intensity of energy sources with CCS technology 1 in power plant s for period i - def CCS_CI_1(model, i, s): - return model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i]) == model.CI_RET_1[i,s] - - model.Cons_7 = pyo.Constraint(i, model.S, rule = CCS_CI_1) - - - #Calculation of carbon intensity of energy sources with CCS technology 2 in power plant s for period i - def CCS_CI_2(model, i, s): - return model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i]) == model.CI_RET_2[i,s] - - model.Cons_8 = pyo.Constraint(i, model.S, rule = CCS_CI_2) - - - #If selected, the deployment of CCS technology 1 in power plant s is limited by the upper bound of the energy output for period i - def CCS_limit_1(model, i, s): - return model.CCS_1[i,s] <= model.plant[s]['UB'] * model.B[i,s] - - model.Cons_9 = pyo.Constraint(i, model.S, rule = CCS_limit_1) - - - #If selected, the deployment of CCS technology 2 in power plant s is limited by the upper bound of the energy output for period i - def CCS_limit_2(model, i, s): - return model.CCS_2[i,s] <= model.plant[s]['UB'] * model.C[i,s] - - model.Cons_10 = pyo.Constraint(i, model.S, rule = CCS_limit_2) - - - #The total CCS deployment in power plant s should be equal to summation of deployment of individual types of CCS technology for period i - def CCS_total(model, i, s): - return model.CCS_1[i,s] + model.CCS_2[i,s] <= model.energy[i,s] - - model.Cons_11 = pyo.Constraint(i, model.S, rule = CCS_total) - - - #The deployment of CCS technology 1 at later periods should at least match the deployment in the previous period - def CCS_1_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.CCS_1[i+1,s] >= model.CCS_1[i,s] - - model.Cons_12 = pyo.Constraint(i, model.S, rule = CCS_1_constraint) - - - #The deployment of CCS technology 2 at later periods should at least match the deployment in the previous period - def CCS_2_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.CCS_2[i+1,s] >= model.CCS_2[i,s] - - model.Cons_13 = pyo.Constraint(i, model.S, rule = CCS_2_constraint) - - - #Determine the net energy available from power plant s with CCS technology 1 for period i - def CCS_1_net_energy(model, i, s): - return model.CCS_1[i,s] * (1 - model.CCS_data['X_1'][i]) == model.net_energy_CCS_1[i,s] - - model.Cons_14 = pyo.Constraint(i, model.S, rule = CCS_1_net_energy) - - - #Determine the net energy available from power plant s with CCS technology 2 for period i - def CCS_2_net_energy(model, i, s): - return model.CCS_2[i,s] * (1 - model.CCS_data['X_2'][i]) == model.net_energy_CCS_2[i,s] - - model.Cons_15 = pyo.Constraint(i, model.S, rule = CCS_2_net_energy) - - - #The deployment of alternative solid fuel technology 1 at later periods should at least match the deployment in the previous period - def alt_solid_1_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.solid_1[i+1,s] >= model.solid_1[i,s] - - model.Cons_16 = pyo.Constraint(i, model.S, rule = alt_solid_1_constraint) - - - #The deployment of alternative solid fuel technology 2 at later periods should at least match the deployment in the previous period - def alt_solid_2_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.solid_2[i+1,s] >= model.solid_2[i,s] - - model.Cons_17 = pyo.Constraint(i, model.S, rule = alt_solid_2_constraint) - - - #The deployment of alternative gas fuel technology 1 at later periods should at least match the deployment in the previous period - def alt_gas_1_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.gas_1[i+1,s] >= model.gas_1[i,s] - - model.Cons_18 = pyo.Constraint(i, model.S, rule = alt_gas_1_constraint) - - - #The deployment of alternative gas fuel technology 2 at later periods should at least match the deployment in the previous period - def alt_gas_2_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.gas_2[i+1,s] >= model.gas_2[i,s] - - model.Cons_19 = pyo.Constraint(i, model.S, rule = alt_gas_2_constraint) - - - #The total energy contribution must equal the initially determined energy contribution of power plant s for period i - def fuel_substitution(model, i, s): - if 'REN' in model.plant[s].values(): - return model.net_energy[i,s] == model.energy[i,s] - elif 'NG' in model.plant[s].values(): - return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] + model.gas_1[i,s] + model.gas_2[i,s] == model.energy[i,s] - elif 'COAL' in model.plant[s].values(): - return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] == model.energy[i,s] - else: - return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] == model.energy[i,s] - - model.Cons_20 = pyo.Constraint(i, model.S, rule = fuel_substitution) - - - #If power plant s is fuelled by renewable energy sources, CCS technology 1 would not be deployed for period i - def no_CCS_1(model, i, s): - if 'REN' in model.plant[s].values(): - return model.CCS_1[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_21 = pyo.Constraint(i, model.S, rule = no_CCS_1) - - - #If power plant s is fuelled by renewable energy sources, CCS technology 1 would not be deployed for period i - def no_CCS_2(model, i, s): - if 'REN' in model.plant[s].values(): - return model.CCS_2[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_22 = pyo.Constraint(i, model.S, rule = no_CCS_2) - - - #Technology implementation time for CCS technology 1 - def deployment_CCS_1(model, i, s): - if model.TIME['CCS_1'][i] == 'NO': - return model.B[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_23 = pyo.Constraint(i, model.S, rule = deployment_CCS_1) - - - #Technology implementation time for CCS technology 2 - def deployment_CCS_2(model, i, s): - if model.TIME['CCS_2'][i] == 'NO': - return model.C[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_24 = pyo.Constraint(i, model.S, rule = deployment_CCS_2) - - - #Technology implementation time for EP-NETs technology 1 - def deployment_EP_NETs_1(model, i): - if model.TIME['EP_NETs_1'][i] == 'NO': - return model.D[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_25 = pyo.Constraint(i, rule = deployment_EP_NETs_1) - - - #Technology implementation time for EP-NETs technology 2 - def deployment_EP_NETs_2(model, i): - if model.TIME['EP_NETs_2'][i] == 'NO': - return model.E[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_26 = pyo.Constraint(i, rule = deployment_EP_NETs_2) - - - #Technology implementation time for EP-NETs technology 3 - def deployment_EP_NETs_3(model, i): - if model.TIME['EP_NETs_3'][i] == 'NO': - return model.F[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_27 = pyo.Constraint(i, rule = deployment_EP_NETs_3) - - - #Technology implementation time for EC-NETs technology 1 - def deployment_EC_NETs_1(model, i): - if model.TIME['EC_NETs_1'][i] == 'NO': - return model.G[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_28 = pyo.Constraint(i, rule = deployment_EC_NETs_1) - - - #Technology implementation time for EC-NETs technology 2 - def deployment_EC_NETs_2(model, i): - if model.TIME['EC_NETs_2'][i] == 'NO': - return model.H[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_29 = pyo.Constraint(i, rule = deployment_EC_NETs_2) - - - #Technology implementation time for EC-NETs technology 3 - def deployment_EC_NETs_3(model, i): - if model.TIME['EC_NETs_3'][i] == 'NO': - return model.I[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_30 = pyo.Constraint(i, rule = deployment_EC_NETs_3) - - - #Technology implementation time for solar compensatory energy - def deployment_solar(model, i): - if model.TIME['SOLAR'][i] == 'NO': - return model.J[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_31 = pyo.Constraint(i, rule = deployment_solar) - - - #Technology implementation time for hydropower compensatory energy - def deployment_hydro(model, i): - if model.TIME['HYDRO'][i] == 'NO': - return model.K[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_32 = pyo.Constraint(i, rule = deployment_hydro) - - - #Technology implementation time for biomass compensatory energy - def deployment_biomass(model, i): - if model.TIME['BIOMASS'][i] == 'NO': - return model.L[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_33 = pyo.Constraint(i, rule = deployment_biomass) - - - #Technology implementation time for biogas compensatory energy - def deployment_biogas(model, i): - if model.TIME['BIOGAS'][i] == 'NO': - return model.M[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_34 = pyo.Constraint(i, rule = deployment_biogas) - - - #Technology implementation time for MSW compensatory energy - def deployment_MSW(model, i): - if model.TIME['MSW'][i] == 'NO': - return model.N[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_35 = pyo.Constraint(i, rule = deployment_MSW) - - - #Technology implementation time for alternative solid fuel type 1 - def deployment_alt_solid_1(model, i, s): - if model.TIME['SOLID_1'][i] == 'NO': - return model.O[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_36 = pyo.Constraint(i, model.S, rule = deployment_alt_solid_1) - - - #Technology implementation time for alternative solid fuel type 2 - def deployment_alt_solid_2(model, i, s): - if model.TIME['SOLID_2'][i] == 'NO': - return model.P[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_37 = pyo.Constraint(i, model.S, rule = deployment_alt_solid_2) - - - #Technology implementation time for alternative gas fuel type 1 - def deployment_alt_gas_1(model, i, s): - if model.TIME['GAS_1'][i] == 'NO': - return model.Q[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_38 = pyo.Constraint(i, model.S, rule = deployment_alt_gas_1) - - - #Technology implementation time for alternative gas fuel type 2 - def deployment_alt_gas_2(model, i, s): - if model.TIME['GAS_2'][i] == 'NO': - return model.R[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_39 = pyo.Constraint(i, model.S, rule = deployment_alt_gas_2) - - - #Big M formulation for EP-NETs technology 1 deployment for period i - def big_M_EP_NETs_1(model, i): - return model.EP_NET_1[i] <= model.D[i] * 1000 - - model.Cons_40 = pyo.Constraint(i, rule = big_M_EP_NETs_1) - - - #Big M formulation for EP-NETs technology 2 deployment for period i - def big_M_EP_NETs_2(model, i): - return model.EP_NET_2[i] <= model.E[i] * 1000 - - model.Cons_41 = pyo.Constraint(i, rule = big_M_EP_NETs_2) - - - #Big M formulation for EP-NETs technology 3 deployment for period i - def big_M_EP_NETs_3(model, i): - return model.EP_NET_3[i] <= model.F[i] * 1000 - - model.Cons_42 = pyo.Constraint(i, rule = big_M_EP_NETs_3) - - - #Big M formulation for EC-NETs technology 1 deployment for period i - def big_M_EC_NETs_1(model, i): - return model.EC_NET_1[i] <= model.G[i] * 1000 - - model.Cons_43 = pyo.Constraint(i, rule = big_M_EC_NETs_1) - - - #Big M formulation for EC-NETs technology 2 deployment for period i - def big_M_EC_NETs_2(model, i): - return model.EC_NET_2[i] <= model.H[i] * 1000 - - model.Cons_44 = pyo.Constraint(i, rule = big_M_EC_NETs_2) - - - #Big M formulation for EC-NETs technology 3 deployment for period i - def big_M_EC_NETs_3(model, i): - return model.EC_NET_3[i] <= model.I[i] * 1000 - - model.Cons_45 = pyo.Constraint(i, rule = big_M_EC_NETs_3) - - - #Big M formulation for deployment of solar renewable energy for period i - def big_M_solar(model, i): - return model.REN_SOLAR[i] <= model.J[i] * 1000 - - model.Cons_46 = pyo.Constraint(i, rule = big_M_solar) - - - #Big M formulation for deployment of hydro renewable energy for period i - def big_M_hydro(model, i): - return model.REN_HYDRO[i] <= model.K[i] * 1000 - - model.Cons_47 = pyo.Constraint(i, rule = big_M_hydro) - - - #Big M formulation for deployment of BM renewable energy for period i - def big_M_biomass(model, i): - return model.REN_BM[i] <= model.L[i] * 1000 - - model.Cons_48 = pyo.Constraint(i, rule = big_M_biomass) - - - #Big M formulation for deployment of BG renewable energy for period i - def big_M_biogas(model, i): - return model.REN_BG[i] <= model.M[i] * 1000 - - model.Cons_49 = pyo.Constraint(i, rule = big_M_biogas) - - - #Big M formulation for deployment of MSW renewable energy for period i - def big_M_MSW(model, i): - return model.REN_MSW[i] <= model.N[i] * 1000 - - model.Cons_50 = pyo.Constraint(i, rule = big_M_MSW) - - - #Big M formulation for deployment of alternative solid fuel type 1 for period i - def big_M_alt_solid_1(model, i, s): - return model.solid_1[i,s] <= model.O[i,s] * model.plant[s]['UB'] - - model.Cons_51 = pyo.Constraint(i, model.S, rule = big_M_alt_solid_1) - - - #Big M formulation for deployment of alternative solid fuel type 2 for period i - def big_M_alt_solid_2(model, i, s): - return model.solid_2[i,s] <= model.P[i,s] * model.plant[s]['UB'] - - model.Cons_52 = pyo.Constraint(i, model.S, rule = big_M_alt_solid_2) - - - #Big M formulation for deployment of alternative gas fuel type 1 for period i - def big_M_alt_gas_1(model, i, s): - return model.gas_1[i,s] <= model.Q[i,s] * model.plant[s]['UB'] - - model.Cons_53 = pyo.Constraint(i, model.S, rule = big_M_alt_gas_1) - - - #Big M formulation for deployment of alternative gas fuel type 2 for period i - def big_M_alt_gas_2(model, i, s): - return model.gas_2[i,s] <= model.R[i,s] * model.plant[s]['UB'] - - model.Cons_54 = pyo.Constraint(i, model.S, rule = big_M_alt_gas_2) - - - #The deployment of solar renewable energy at later periods should at least match the deployment in the previous period - def solar_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_SOLAR[i+1] >= model.REN_SOLAR[i] - - model.Cons_55 = pyo.Constraint(i, model.S, rule = solar_time_constraint) - - - #The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period - def hydro_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_HYDRO[i+1] >= model.REN_HYDRO[i] - - model.Cons_56 = pyo.Constraint(i, model.S, rule = hydro_time_constraint) - - - #The deployment of solar renewable energy at later periods should at least match the deployment in the previous period - def biomass_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_BM[i+1] >= model.REN_BM[i] - - model.Cons_57 = pyo.Constraint(i, model.S, rule = biomass_time_constraint) - - -#The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period - def biogas_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_BG[i+1] >= model.REN_BG[i] - - model.Cons_58 = pyo.Constraint(i, model.S, rule = biogas_time_constraint) - - - #The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period - def MSW_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_MSW[i+1] >= model.REN_MSW[i] - - model.Cons_59 = pyo.Constraint(i, model.S, rule = MSW_time_constraint) - - - #The deployment of EP-NETs technology 1 at later periods should at least match the deployment in the previous period - def EP_NETs_1_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EP_NET_1[i+1] >= model.EP_NET_1[i] - - model.Cons_60 = pyo.Constraint(i, model.S, rule = EP_NETs_1_time_constraint) - - - #The deployment of EP-NETs technology 2 at later periods should at least match the deployment in the previous period - def EP_NETs_2_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EP_NET_2[i+1] >= model.EP_NET_2[i] - - model.Cons_61 = pyo.Constraint(i, model.S, rule = EP_NETs_2_time_constraint) - - - #The deployment of EP-NETs technology 3 at later periods should at least match the deployment in the previous period - def EP_NETs_3_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EP_NET_3[i+1] >= model.EP_NET_3[i] - - model.Cons_62 = pyo.Constraint(i, model.S, rule = EP_NETs_3_time_constraint) - - - #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period - def EC_NETs_1_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EC_NET_1[i+1] >= model.EC_NET_1[i] - - model.Cons_63 = pyo.Constraint(i, model.S, rule = EC_NETs_1_time_constraint) - - - #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period - def EC_NETs_2_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EC_NET_2[i+1] >= model.EC_NET_2[i] - - model.Cons_64 = pyo.Constraint(i, model.S, rule = EC_NETs_2_time_constraint) - - - #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period - def EC_NETs_3_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EC_NET_3[i+1] >= model.EC_NET_3[i] - - model.Cons_65 = pyo.Constraint(i, model.S, rule = EC_NETs_3_time_constraint) - - - #Total energy contribution from all energy sources to satisfy the total demand for period i - def total_energy(model, i, s): - return sum((model.net_energy[i,s] + model.net_energy_CCS_1[i,s] + model.net_energy_CCS_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] + model.gas_1[i,s] + model.gas_2[i,s]) for s in model.S) + model.REN_SOLAR[i] + model.REN_HYDRO[i] + model.REN_BM[i] + model.REN_BG[i] + model.REN_MSW[i] + model.EP_NET_1[i] + model.EP_NET_2[i] + model.EP_NET_3[i] == model.EP['Demand'][i] + model.EC_NET_1[i] + model.EC_NET_2[i] + model.EC_NET_3[i] - - model.Cons_66 = pyo.Constraint(i, model.S, rule = total_energy) - - - #The total CO2 load contribution from all energy sources must satisfy most the CO2 emission limit in period i - def total_CO2_load(model, i, s): - return (sum((model.net_energy[i,s] * model.plant[s]['CI']) + (model.net_energy_CCS_1[i,s] * model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i])) + (model.net_energy_CCS_2[i,s] * model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i])) - + (model.solid_1[i,s] * model.SLD_CI['SOLID_1'][i]) + (model.solid_2[i,s] * model.SLD_CI['SOLID_2'][i]) - + (model.gas_1[i,s] * model.GAS_CI['GAS_1'][i]) + (model.gas_2[i,s] * model.GAS_CI['GAS_2'][i]) for s in model.S) - + (model.EC_NET_1[i] * model.NET_CI['EC_NETs_1'][i]) - + (model.EC_NET_2[i] * model.NET_CI['EC_NETs_2'][i]) - + (model.EC_NET_3[i] * model.NET_CI['EC_NETs_3'][i]) - + (model.EP_NET_1[i] * model.NET_CI['EP_NETs_1'][i]) - + (model.EP_NET_2[i] * model.NET_CI['EP_NETs_2'][i]) - + (model.EP_NET_3[i] * model.NET_CI['EP_NETs_3'][i]) - + (model.REN_SOLAR[i] * model.REN_CI['SOLAR'][i]) - + (model.REN_HYDRO[i] * model.REN_CI['HYDRO'][i]) - + (model.REN_BM[i] * model.REN_CI['BIOMASS'][i]) - + (model.REN_BG[i] * model.REN_CI['BIOGAS'][i]) - + (model.REN_MSW[i] * model.REN_CI['MSW'][i]) == model.new_emission[i]) - - model.Cons_67 = pyo.Constraint(i, model.S, rule = total_CO2_load) - - - #Determining the cumulative total fuel and annualised capital cost for all power plants for period i - def energy_cost(model, i, s): - if 'SOLAR' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['SOLAR'][i]) - elif 'HYDRO' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['HYDRO'][i]) - elif 'BIOGAS' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOGAS'][i]) - elif 'BIOMASS' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOMASS'][i]) - elif 'MSW' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['MSW'][i]) - elif 'NG' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['NG'][i]) + (AFF * model.CPX_1['NG'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['NG'][i]) - elif 'OIL' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['OIL'][i]) + (AFF * model.CPX_1['OIL'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['OIL'][i]) - else: - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['COAL'][i]) + (AFF * model.CPX_1['COAL'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['COAL'][i]) - - model.Cons_68 = pyo.Constraint(i, model.S, rule = energy_cost) - - - #The summation of cost for each power plant s should equal to the total cost of each period i - def sum_cost(model, i, s): - return (sum((model.net_energy_CCS_1[i,s] * model.CCS_data['Cost_CCS_1'][i]) + (model.net_energy_CCS_2[i,s] * model.CCS_data['Cost_CCS_2'][i]) - + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]) - + (model.solid_1[i,s] * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]) + (model.solid_2[i,s] * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]) - + (model.gas_1[i,s] * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]) + (model.gas_2[i,s] * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]) for s in model.S) - + (model.EC_NET_1[i] * model.NET_COST['EC_NETs_1'][i]) + (AFF * model.CPX_1['EC_NETs_1'][i] * model.G[i]) + (AFF * model.EC_NET_1[i] * model.CPX_2['EC_NETs_1'][i]) - + (model.EC_NET_2[i] * model.NET_COST['EC_NETs_2'][i]) + (AFF * model.CPX_1['EC_NETs_2'][i] * model.H[i]) + (AFF * model.EC_NET_2[i] * model.CPX_2['EC_NETs_2'][i]) - + (model.EC_NET_3[i] * model.NET_COST['EC_NETs_3'][i]) + (AFF * model.CPX_1['EC_NETs_3'][i] * model.I[i]) + (AFF * model.EC_NET_3[i] * model.CPX_2['EC_NETs_3'][i]) - + (model.EP_NET_1[i] * model.NET_COST['EP_NETs_1'][i]) + (AFF * model.CPX_1['EP_NETs_1'][i] * model.D[i]) + (AFF * model.EP_NET_1[i] * model.CPX_2['EP_NETs_1'][i]) - + (model.EP_NET_2[i] * model.NET_COST['EP_NETs_2'][i]) + (AFF * model.CPX_1['EP_NETs_2'][i] * model.E[i]) + (AFF * model.EP_NET_2[i] * model.CPX_2['EP_NETs_2'][i]) - + (model.EP_NET_3[i] * model.NET_COST['EP_NETs_3'][i]) + (AFF * model.CPX_1['EP_NETs_3'][i] * model.F[i]) + (AFF * model.EP_NET_3[i] * model.CPX_2['EP_NETs_3'][i]) - + (model.REN_SOLAR[i] * model.REN_COST['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.J[i]) + (AFF * model.REN_SOLAR[i] * model.CPX_2['SOLAR'][i]) - + (model.REN_HYDRO[i] * model.REN_COST['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.K[i]) + (AFF * model.REN_HYDRO[i] * model.CPX_2['HYDRO'][i]) - + (model.REN_BM[i] * model.REN_COST['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.L[i]) + (AFF * model.REN_BM[i] * model.CPX_2['BIOMASS'][i]) - + (model.REN_BG[i] * model.REN_COST['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.M[i]) + (AFF * model.REN_BG[i] * model.CPX_2['BIOGAS'][i]) - + (model.REN_MSW[i] * model.REN_COST['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.N[i]) + (AFF * model.REN_MSW[i] * model.CPX_2['MSW'][i]) - + sum(model.energy_cost[i,s] for s in model.S) == model.sum_cost[i]) - - model.Cons_69 = pyo.Constraint(i, model.S, rule = sum_cost) - - - #For the minimum budget objective function, the total cost is minimised, subject to the satisfaction of the CO2 emission limit - #For the minimum emission objective function, the total emission is minimised, subject to the available budgetary constraint - def objective_constraint(model, i): - if flag == 'min_budget': - return model.new_emission[i] <= model.EP['Limit'][i] - else: - return model.sum_cost[i] <= model.EP['Budget'][i] - - model.Cons_70 = pyo.Constraint(i, rule = objective_constraint) - - - #opt = SolverFactory('octeract-engine', tee = True) - #results = opt.solve(model) - - opt = SolverFactory('gams') - #sys.exit() - results = opt.solve(model, solver = 'cplex') - - #opt = SolverFactory('gurobi', solver_io = 'python') - #results = opt.solve(model) - - print(results) - #model.pprint() - return model - -def multiperiod_energy_planning_results(model, i): - energy_planning = pd.DataFrame() - - for s in model.plant.keys(): - energy_planning.loc[s, 'Fuel'] = model.plant[s]['Fuel'] - energy_planning.loc[s, 'Energy Generation'] = model.A[i,s]() - energy_planning.loc[s, 'Gross Energy (TWh/y)'] = round(model.energy[i,s](), 2) - energy_planning.loc[s, 'CO2 Intensity (Mt/TWh)'] = model.plant[s]['CI'] - #energy_planning.loc[s, 'CCS_1 CI'] = round(model.CI_RET_1[i,s](), 3) - #energy_planning.loc[s, 'CCS_2 CI'] = round(model.CI_RET_2[i,s](), 3) - energy_planning.loc[s, 'CCS_1 Selection'] = model.B[i,s]() - energy_planning.loc[s, 'CCS_1 Ret (TWh/y)'] = round(model.CCS_1[i,s](), 2) - energy_planning.loc[s, 'CCS_2 Selection'] = model.C[i,s]() - energy_planning.loc[s, 'CCS_2 Ret (TWh/y)'] = round(model.CCS_2[i,s](), 2) - #energy_planning.loc[s, 'Net Energy wo CCS'] = round(model.net_energy[i,s](), 2) - #energy_planning.loc[s, 'Net Energy w CCS_1'] = round(model.net_energy_CCS_1[i,s](), 2) - #energy_planning.loc[s, 'Net Energy w CCS_2'] = round(model.net_energy_CCS_2[i,s](), 2) - energy_planning.loc[s, 'Solid_1 Selection'] = model.O[i,s]() - energy_planning.loc[s, 'SOLID_1 (TWh/y)'] = round(model.solid_1[i,s](), 2) - energy_planning.loc[s, 'Solid_2 Selection'] = model.P[i,s]() - energy_planning.loc[s, 'SOLID_2 (TWh/y)'] = round(model.solid_2[i,s](), 2) - energy_planning.loc[s, 'Gas_1 Selection'] = model.Q[i,s]() - energy_planning.loc[s, 'GAS_1 (TWh/y)'] = round(model.gas_1[i,s](), 2) - energy_planning.loc[s, 'Gas_2 Selection'] = model.R[i,s]() - energy_planning.loc[s, 'GAS_2 (TWh/y)'] = round(model.gas_2[i,s](), 2) - energy_planning.loc[s, 'Net Energy (TWh/y)'] = round(model.net_energy[i,s]() + model.net_energy_CCS_1[i,s]() + model.net_energy_CCS_2[i,s]() + model.solid_1[i,s]() + model.solid_2[i,s]() + model.gas_1[i,s]() + model.gas_2[i,s](), 2) - energy_planning.loc[s, 'CO2 Load (Mt/y)'] = round((model.net_energy[i,s]() * model.plant[s]['CI']) + (model.net_energy_CCS_1[i,s]() * model.CI_RET_1[i,s]()) + (model.net_energy_CCS_2[i,s]() * model.CI_RET_2[i,s]()) + (model.solid_1[i,s]() * model.SLD_CI['SOLID_1'][i]) + (model.solid_2[i,s]() * model.SLD_CI['SOLID_2'][i]) + (model.gas_1[i,s]() * model.GAS_CI['GAS_1'][i]) + (model.gas_2[i,s]() * model.GAS_CI['GAS_2'][i]), 2) - - energy_planning.loc['EP_NET_1', 'Fuel'] = 'EP_NET_1' - energy_planning.loc['EP_NET_2', 'Fuel'] = 'EP_NET_2' - energy_planning.loc['EP_NET_3', 'Fuel'] = 'EP_NET_3' - energy_planning.loc['EC_NET_1', 'Fuel'] = 'EC_NET_1' - energy_planning.loc['EC_NET_2', 'Fuel'] = 'EC_NET_2' - energy_planning.loc['EC_NET_3', 'Fuel'] = 'EC_NET_3' - energy_planning.loc['EP_NET_1', 'Energy Generation'] = model.D[i]() - energy_planning.loc['EP_NET_2', 'Energy Generation'] = model.E[i]() - energy_planning.loc['EP_NET_3', 'Energy Generation'] = model.F[i]() - energy_planning.loc['EC_NET_1', 'Energy Generation'] = model.G[i]() - energy_planning.loc['EC_NET_2', 'Energy Generation'] = model.H[i]() - energy_planning.loc['EC_NET_3', 'Energy Generation'] = model.I[i]() - energy_planning.loc['EP_NET_1', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_1'][i] - energy_planning.loc['EP_NET_2', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_2'][i] - energy_planning.loc['EP_NET_3', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_3'][i] - energy_planning.loc['EC_NET_1', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_1'][i] - energy_planning.loc['EC_NET_2', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_2'][i] - energy_planning.loc['EC_NET_3', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_3'][i] - energy_planning.loc['EP_NET_1', 'Net Energy (TWh/y)'] = round(model.EP_NET_1[i](), 2) - energy_planning.loc['EP_NET_2', 'Net Energy (TWh/y)'] = round(model.EP_NET_2[i](), 2) - energy_planning.loc['EP_NET_3', 'Net Energy (TWh/y)'] = round(model.EP_NET_3[i](), 2) - energy_planning.loc['EC_NET_1', 'Net Energy (TWh/y)'] = round(model.EC_NET_1[i](), 2) - energy_planning.loc['EC_NET_2', 'Net Energy (TWh/y)'] = round(model.EC_NET_2[i](), 2) - energy_planning.loc['EC_NET_3', 'Net Energy (TWh/y)'] = round(model.EC_NET_3[i](), 2) - energy_planning.loc['EP_NET_1', 'CO2 Load (Mt/y)'] = round(model.EP_NET_1[i]() * model.NET_CI['EP_NETs_1'][i], 2) - energy_planning.loc['EP_NET_2', 'CO2 Load (Mt/y)'] = round(model.EP_NET_2[i]() * model.NET_CI['EP_NETs_2'][i], 2) - energy_planning.loc['EP_NET_3', 'CO2 Load (Mt/y)'] = round(model.EP_NET_3[i]() * model.NET_CI['EP_NETs_3'][i], 2) - energy_planning.loc['EC_NET_1', 'CO2 Load (Mt/y)'] = round(model.EC_NET_1[i]() * model.NET_CI['EC_NETs_1'][i], 2) - energy_planning.loc['EC_NET_2', 'CO2 Load (Mt/y)'] = round(model.EC_NET_2[i]() * model.NET_CI['EC_NETs_2'][i], 2) - energy_planning.loc['EC_NET_3', 'CO2 Load (Mt/y)'] = round(model.EC_NET_3[i]() * model.NET_CI['EC_NETs_3'][i], 2) - energy_planning.loc['SOLAR', 'Fuel'] = 'SOLAR' - energy_planning.loc['HYDRO', 'Fuel'] = 'HYDRO' - energy_planning.loc['BIOMASS', 'Fuel'] = 'BIOMASS' - energy_planning.loc['BIOGAS', 'Fuel'] = 'BIOGAS' - energy_planning.loc['MSW', 'Fuel'] = 'MSW' - energy_planning.loc['SOLAR', 'Energy Generation'] = model.J[i]() - energy_planning.loc['HYDRO', 'Energy Generation'] = model.K[i]() - energy_planning.loc['BIOMASS', 'Energy Generation'] = model.L[i]() - energy_planning.loc['BIOGAS', 'Energy Generation'] = model.M[i]() - energy_planning.loc['MSW', 'Energy Generation'] = model.N[i]() - energy_planning.loc['SOLAR', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['SOLAR'][i] - energy_planning.loc['HYDRO', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['HYDRO'][i] - energy_planning.loc['BIOMASS', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['BIOMASS'][i] - energy_planning.loc['BIOGAS', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['BIOGAS'][i] - energy_planning.loc['MSW', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['MSW'][i] - energy_planning.loc['SOLAR', 'Net Energy (TWh/y)'] = round(model.REN_SOLAR[i](),2) - energy_planning.loc['SOLAR', 'CO2 Load (Mt/y)'] = round(model.REN_SOLAR[i]() * model.REN_CI['SOLAR'][i], 2) - energy_planning.loc['HYDRO', 'Net Energy (TWh/y)'] = round(model.REN_HYDRO[i](),2) - energy_planning.loc['HYDRO', 'CO2 Load (Mt/y)'] = round(model.REN_HYDRO[i]() * model.REN_CI['HYDRO'][i], 2) - energy_planning.loc['BIOMASS', 'Net Energy (TWh/y)'] = round(model.REN_BM[i](),2) - energy_planning.loc['BIOMASS', 'CO2 Load (Mt/y)'] = round(model.REN_BM[i]() * model.REN_CI['BIOMASS'][i], 2) - energy_planning.loc['BIOGAS', 'Net Energy (TWh/y)'] = round(model.REN_BG[i](),2) - energy_planning.loc['BIOGAS', 'CO2 Load (Mt/y)'] = round(model.REN_BG[i]() * model.REN_CI['BIOGAS'][i], 2) - energy_planning.loc['MSW', 'Net Energy (TWh/y)'] = round(model.REN_MSW[i](),2) - energy_planning.loc['MSW', 'CO2 Load (Mt/y)'] = round(model.REN_MSW[i]() * model.REN_CI['MSW'][i], 2) - energy_planning.loc['TOTAL', 'CO2 Load (Mt/y)'] = round(model.new_emission[i](), 2) - energy_planning.loc['TOTAL', 'Total Cost (mil USD/y)'] = round(model.sum_cost[i](), 2) - - writer = pd.ExcelWriter(file_name, engine = 'openpyxl', mode = 'a', if_sheet_exists = 'new') - #writer = pd.ExcelWriter(file_name, mode = 'a', if_sheet_exists = 'new') - energy_planning.to_excel(writer, sheet_name = 'Results_Period_1') - writer.save() - - return - - -model = multiperiod_energy_planning(model, periods) - -''' -for i in list(range(1, numperiods,1)): - multiperiod_energy_planning_results(model,i) -''' \ No newline at end of file From 156e77b0785f081516692be7928f3f3de6b50260 Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 00:34:02 +0500 Subject: [PATCH 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Q`1cX8U5>-}b^iMQ09x_=H2?qr From cdb3d3e536d1a2cc111eb136eae2efb7bc21c2f7 Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 00:34:17 +0500 Subject: [PATCH 03/24] Delete Industry_Model_Python.py --- Industry_Model_Python.py | 1049 -------------------------------------- 1 file changed, 1049 deletions(-) delete mode 100644 Industry_Model_Python.py diff --git a/Industry_Model_Python.py b/Industry_Model_Python.py deleted file mode 100644 index 4faf6e6..0000000 --- a/Industry_Model_Python.py +++ /dev/null @@ -1,1049 +0,0 @@ -''' -Created on 30th June 2022 - -Energy Planning Scenario in Pyomo - -This script presents the mathematical optimisation formulation concerning -carbon-constrained energy planning for a specific geographical region or -district. - -Power plants are fuelled by either renewable energy sources e.g., solar, -hydropower etc or fossil-based sources e.g., coal, natural gas etc. - -This energy planning is conducted for several periods (user-specified), with -each period having its respective demand and CO2 emission limit - -The satisfaction of the CO2 load limit is via the deployment of compensatory -energy, CCS technology, alternative low-carbon fuels (replacing fossil fuels), -and negative emission technologies (NETs) - -NETs are either made up of energy-producing NETs (EP-NETs) or energy-consuming -NETs (EC-NETs). Examples of EP-NETs are biomass and biochar, while EC-NETs -are made up of enhanced weathering, direct air capture etc. - -EP-NETs and EC-NETs produce and consume energy during CO2 removal respectively - -Alternative solid and gas fuels have low CO2 intensity, with a potential of -replacing coal and natural gas in fossil-based power plants respectively - -@author: Purusothmn, Dr Michael Short - -''' -import pyomo.environ as pyo -from pyomo.opt import SolverFactory -import pandas as pd -import os -from openpyxl import load_workbook - -cwd = os.getcwd() -model = pyo.ConcreteModel() - -file_name = r'Industry_User_Interface_v1.xlsx' -model.plant = pd.read_excel(file_name, sheet_name = 'PLANT_DATA', index_col = 0, header = 32, nrows = 7).to_dict() -model.EP = pd.read_excel(file_name, sheet_name = 'ENERGY_PLANNING_DATA', index_col = 0, header = 7).to_dict() -model.fuel = pd.read_excel(file_name, sheet_name = 'FUEL_COST_DATA', index_col = 0, header = 12).to_dict() -model.REN_CI = pd.read_excel(file_name, sheet_name = 'RENEWABLE_CI_DATA', index_col = 0, header = 9).to_dict() -model.REN_COST = pd.read_excel(file_name, sheet_name = 'RENEWABLE_COST_DATA', index_col = 0, header = 9).to_dict() -model.CPX_1 = pd.read_excel(file_name, sheet_name = 'CAPEX_DATA_1', index_col = 0, header = 20).to_dict() -model.CPX_2 = pd.read_excel(file_name, sheet_name = 'CAPEX_DATA_2', index_col = 0, header = 20).to_dict() -model.SLD_CI = pd.read_excel(file_name, sheet_name = 'ALT_SOLID_CI', index_col = 0, header = 6).to_dict() -model.SLD_COST = pd.read_excel(file_name, sheet_name = 'ALT_SOLID_COST', index_col = 0, header = 6).to_dict() -model.GAS_CI = pd.read_excel(file_name, sheet_name = 'ALT_GAS_CI', index_col = 0, header = 6).to_dict() -model.GAS_COST = pd.read_excel(file_name, sheet_name = 'ALT_GAS_COST', index_col = 0, header = 6).to_dict() -model.CCS_data = pd.read_excel(file_name, sheet_name = 'CCS_DATA', index_col = 0, header = 12).to_dict() -model.NET_CI = pd.read_excel(file_name, sheet_name = 'NET_CI_DATA', index_col = 0, header = 12).to_dict() -model.NET_COST = pd.read_excel(file_name, sheet_name = 'NET_COST_DATA', index_col = 0, header = 12).to_dict() -model.TIME = pd.read_excel(file_name, sheet_name = 'TECH_IMPLEMENTATION_TIME', index_col = 0, header = 19).to_dict() - -wb = load_workbook(file_name) - -sheet_1 = wb['PLANT_DATA'] -flag = sheet_1['B30'].value - -numperiods = sheet_1['B31'].value + 1 - -sheet_2 = wb['CAPEX_DATA_1'] -AFF = sheet_2['B18'].value - -periods = list(range(1,numperiods,1)) - -def multiperiod_energy_planning(model, i): - model.S = model.plant.keys() - - #LIST OF VARIABLES - #This variable determines the deployment of energy sources in power plant s for period i - model.energy = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the minimum electricty to be purchased for period i - model.electricity = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the CO2 intensity of energy sources in power plant s with CCS technology 1 for period i - model.CI_RET_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the CO2 intensity of energy sources in power plant s with CCS technology 2 for period i - model.CI_RET_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #Binary variable for power generation by power plant s for period i - model.A = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of CCS technology 1 in power plant s for period i - model.B = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of CCS technology 2 in power plant s for period i - model.C = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of EP-NETs technology 1 for period i - model.D = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EP-NETs technology 2 for period i - model.E = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EP-NETs technology 3 for period i - model.F = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EC-NETs technology 1 for period i - model.G = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EC-NETs technology 2 for period i - model.H = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EC-NETs technology 3 for period i - model.I = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of solar renewable energy for period i - model.J = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of hydropower renewable energy for period i - model.K = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of biomass renewable energy for period i - model.L = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of biogas renewable energy for period i - model.M = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of MSW renewable energy for period i - model.N = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of alternative solid-based fuel technology 1 in power plant s for period i - model.O = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of alternative solid-based fuel technology 2 in power plant s for period i - model.P = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of alternative gas-based fuel technology 1 in power plant s for period i - model.Q = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of alternative gas-based fuel technology 2 in power plant s for period i - model.R = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of electricity for period i - model.T = pyo.Var(i, domain = pyo.Binary) - - #This variable represents the deployment of CCS technology 1 in power plant s for period i - model.CCS_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable represents the deployment of CCS technology 2 in power plant s for period i - model.CCS_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the net energy available from power plant s without CCS deployment for period i - model.net_energy = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the net energy available from power plant s with the deployment of CCS technology 1 for period i - model.net_energy_CCS_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the net energy available from power plant s with the deployment of CCS technology 2 for period i - model.net_energy_CCS_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EP_NETs technology 1 for period i - model.EP_NET_1 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EP_NETs technology 2 for period i - model.EP_NET_2 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EP_NETs technology 3 for period i - model.EP_NET_3 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EC_NETs technology 1 for period i - model.EC_NET_1 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EC_NETs technology 2 for period i - model.EC_NET_2 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EC_NETs technology 3 for period i - model.EC_NET_3 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of solar renewable energy for period i - model.REN_SOLAR = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of hydro renewable energy for period i - model.REN_HYDRO = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of biomass renewable energy for period i - model.REN_BM = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of biogas renewable energy for period i - model.REN_BG = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of MSW renewable energy for period i - model.REN_MSW = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of alternative solid-based fuel technology 1 for coal-based plant s for period i - model.solid_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of alternative solid-based fuel technology 2 for coal-based plant s for period i - model.solid_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of alternative gas-based fuel technology 1 for natural gas-based plant s for period i - model.gas_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of alternative gas-based fuel technology 2 for natural gas-based plant s for period i - model.gas_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the revised total CO2 emissions for period i - model.new_emission = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the total energy cost of power plant s for period i - model.energy_cost = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) - - #This variable determines the total energy planning cost for period i - model.sum_cost = pyo.Var(i, domain = pyo.NonNegativeReals) - - - #OBJECTIVE FUNCTION - - #For the minimum budget objective function, the total cost is minimised, subject to the satisfaction of the CO2 emission limit - #For the minimum emission objective function, the total emission is minimised, subject to the available budgetary constraint - if flag == 'min_budget': - model.obj = pyo.Objective(expr = sum(model.sum_cost[i] for i in periods), sense = pyo.minimize) - else: - model.obj = pyo.Objective(expr = sum(model.new_emission[i] for i in periods), sense = pyo.minimize) - - #CONSTRAINTS - - - #Prior to any energy planning, the total power generation from fuel oil, natural gas and biomass should satisfy the thermal demand for period i - def demand_thermal(model, i, s): - return model.EP['Thermal'][i] == model.energy[i,'Fuel Oil'] + model.energy[i,'Natural Gas'] + model.energy[i,'EFB'] + model.energy[i,'PKS'] - - model.Cons_1 = pyo.Constraint(i, model.S, rule = demand_thermal) - - - #Prior to any energy planning, the total power generation from solar, biomass and electricity should satisfy the power demand for period i - def demand_power(model, i, s): - return model.EP['Power'][i] == model.energy[i,'Solar Power'] + model.energy[i,'EFB'] + model.energy[i,'PKS'] + model.electricity[i] - - model.Cons_A = pyo.Constraint(i, model.S, rule = demand_power) - - - #The deployment of energy source in power plant s should at least satisfy the lower bound for period i - def lower_bound_energy(model, i, s): - return model.energy[i,s] >= model.plant[s]['LB'] * model.A[i,s] - - model.Cons_2 = pyo.Constraint(i, model.S, rule = lower_bound_energy) - - - #The deployment of energy source in power plant s should at most satisfy the upper bound for period i - def upper_bound_energy(model, i, s): - return model.energy[i,s] <= model.plant[s]['UB'] * model.A[i,s] - - model.Cons_3 = pyo.Constraint(i, model.S, rule = upper_bound_energy) - - - #There should not be power generation from power plant s before its commissioning period - def plant_commission(model, i, s): - if i < model.plant[s]['ON']: - return model.energy[i,s] == 0 - else: - return model.energy[i,s] >= 0 - - model.Cons_4 = pyo.Constraint(i, model.S, rule = plant_commission) - - - #There should be power generation from power plant s after its decommissioning period - def plant_decommission(model, i, s): - if i >= model.plant[s]['OFF']: - return model.energy[i,s] == 0 - else: - return model.energy[i,s] >= 0 - - model.Cons_5 = pyo.Constraint(i, model.S, rule = plant_decommission) - - ''' - #If power plant s is decomissioned in a period, it should remain decommissioned at later periods - #However, power plant s should be available for power generation till the period before its decommissioning period - def energy_constraint(model, i, s): - if i <= model.plant[s]['OFF'] - 2: - return model.energy[i+1,s] >= model.energy[i,s] - else: - return pyo.Constraint.Skip - - model.Cons_6 = pyo.Constraint(i, model.S, rule = energy_constraint) - ''' - - #The deployment of PKS in period i should at least match its deployment in the previous period - def PKS_energy(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.energy[i+1,'PKS'] >= model.energy[i,'PKS'] - - model.Cons_B = pyo.Constraint(i, model.S, rule = PKS_energy) - - - #The deployment of EFB in period i should at least match its deployment in the previous period - def EFB_energy(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.energy[i+1,'EFB'] >= model.energy[i,'EFB'] - - model.Cons_C = pyo.Constraint(i, model.S, rule = EFB_energy) - - - #The deployment of solar power in period i should at least match its deployment in the previous period - def solar_energy(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.energy[i+1,'Solar Power'] >= model.energy[i,'Solar Power'] - - model.Cons_D = pyo.Constraint(i, model.S, rule = solar_energy) - - - #Calculation of carbon intensity of energy sources with CCS technology 1 in power plant s for period i - def CCS_CI_1(model, i, s): - return model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i]) == model.CI_RET_1[i,s] - - model.Cons_7 = pyo.Constraint(i, model.S, rule = CCS_CI_1) - - - #Calculation of carbon intensity of energy sources with CCS technology 2 in power plant s for period i - def CCS_CI_2(model, i, s): - return model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i]) == model.CI_RET_2[i,s] - - model.Cons_8 = pyo.Constraint(i, model.S, rule = CCS_CI_2) - - - #If selected, the deployment of CCS technology 1 in power plant s is limited by the upper bound of the energy output for period i - def CCS_limit_1(model, i, s): - return model.CCS_1[i,s] <= model.plant[s]['UB'] * model.B[i,s] - - model.Cons_9 = pyo.Constraint(i, model.S, rule = CCS_limit_1) - - - #If selected, the deployment of CCS technology 2 in power plant s is limited by the upper bound of the energy output for period i - def CCS_limit_2(model, i, s): - return model.CCS_2[i,s] <= model.plant[s]['UB'] * model.C[i,s] - - model.Cons_10 = pyo.Constraint(i, model.S, rule = CCS_limit_2) - - - #The total CCS deployment in power plant s should be equal to summation of deployment of individual types of CCS technology for period i - def CCS_total(model, i, s): - return model.CCS_1[i,s] + model.CCS_2[i,s] <= model.energy[i,s] - - model.Cons_11 = pyo.Constraint(i, model.S, rule = CCS_total) - - - #The deployment of CCS technology 1 at later periods should at least match the deployment in the previous period - def CCS_1_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.CCS_1[i+1,s] >= model.CCS_1[i,s] - - model.Cons_12 = pyo.Constraint(i, model.S, rule = CCS_1_constraint) - - - #The deployment of CCS technology 2 at later periods should at least match the deployment in the previous period - def CCS_2_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.CCS_2[i+1,s] >= model.CCS_2[i,s] - - model.Cons_13 = pyo.Constraint(i, model.S, rule = CCS_2_constraint) - - - #Determine the net energy available from power plant s with CCS technology 1 for period i - def CCS_1_net_energy(model, i, s): - return model.CCS_1[i,s] * (1 - model.CCS_data['X_1'][i]) == model.net_energy_CCS_1[i,s] - - model.Cons_14 = pyo.Constraint(i, model.S, rule = CCS_1_net_energy) - - - #Determine the net energy available from power plant s with CCS technology 2 for period i - def CCS_2_net_energy(model, i, s): - return model.CCS_2[i,s] * (1 - model.CCS_data['X_2'][i]) == model.net_energy_CCS_2[i,s] - - model.Cons_15 = pyo.Constraint(i, model.S, rule = CCS_2_net_energy) - - - #The deployment of alternative solid fuel technology 1 at later periods should at least match the deployment in the previous period - def alt_solid_1_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.solid_1[i+1,s] >= model.solid_1[i,s] - - model.Cons_16 = pyo.Constraint(i, model.S, rule = alt_solid_1_constraint) - - - #The deployment of alternative solid fuel technology 2 at later periods should at least match the deployment in the previous period - def alt_solid_2_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.solid_2[i+1,s] >= model.solid_2[i,s] - - model.Cons_17 = pyo.Constraint(i, model.S, rule = alt_solid_2_constraint) - - - #The deployment of alternative gas fuel technology 1 at later periods should at least match the deployment in the previous period - def alt_gas_1_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.gas_1[i+1,s] >= model.gas_1[i,s] - - model.Cons_18 = pyo.Constraint(i, model.S, rule = alt_gas_1_constraint) - - - #The deployment of alternative gas fuel technology 2 at later periods should at least match the deployment in the previous period - def alt_gas_2_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.gas_2[i+1,s] >= model.gas_2[i,s] - - model.Cons_19 = pyo.Constraint(i, model.S, rule = alt_gas_2_constraint) - - - #The total energy contribution must equal the initially determined energy contribution of power plant s for period i - def fuel_substitution(model, i, s): - if 'REN' in model.plant[s].values(): - return model.net_energy[i,s] == model.energy[i,s] - elif 'NG' in model.plant[s].values(): - return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] + model.gas_1[i,s] + model.gas_2[i,s] == model.energy[i,s] - elif 'FUEL OIL' in model.plant[s].values(): - return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] == model.energy[i,s] - else: - return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] == model.energy[i,s] - - model.Cons_20 = pyo.Constraint(i, model.S, rule = fuel_substitution) - - - #If power plant s is fuelled by renewable energy sources, CCS technology 1 would not be deployed for period i - def no_CCS_1(model, i, s): - if 'REN' in model.plant[s].values(): - return model.CCS_1[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_21 = pyo.Constraint(i, model.S, rule = no_CCS_1) - - - #If power plant s is fuelled by renewable energy sources, CCS technology 1 would not be deployed for period i - def no_CCS_2(model, i, s): - if 'REN' in model.plant[s].values(): - return model.CCS_2[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_22 = pyo.Constraint(i, model.S, rule = no_CCS_2) - - - #Technology implementation time for CCS technology 1 - def deployment_CCS_1(model, i, s): - if model.TIME['CCS_1'][i] == 'NO': - return model.B[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_23 = pyo.Constraint(i, model.S, rule = deployment_CCS_1) - - - #Technology implementation time for CCS technology 2 - def deployment_CCS_2(model, i, s): - if model.TIME['CCS_2'][i] == 'NO': - return model.C[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_24 = pyo.Constraint(i, model.S, rule = deployment_CCS_2) - - - #Technology implementation time for EP-NETs technology 1 - def deployment_EP_NETs_1(model, i): - if model.TIME['EP_NETs_1'][i] == 'NO': - return model.D[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_25 = pyo.Constraint(i, rule = deployment_EP_NETs_1) - - - #Technology implementation time for EP-NETs technology 2 - def deployment_EP_NETs_2(model, i): - if model.TIME['EP_NETs_2'][i] == 'NO': - return model.E[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_26 = pyo.Constraint(i, rule = deployment_EP_NETs_2) - - - #Technology implementation time for EP-NETs technology 3 - def deployment_EP_NETs_3(model, i): - if model.TIME['EP_NETs_3'][i] == 'NO': - return model.F[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_27 = pyo.Constraint(i, rule = deployment_EP_NETs_3) - - - #Technology implementation time for EC-NETs technology 1 - def deployment_EC_NETs_1(model, i): - if model.TIME['EC_NETs_1'][i] == 'NO': - return model.G[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_28 = pyo.Constraint(i, rule = deployment_EC_NETs_1) - - - #Technology implementation time for EC-NETs technology 2 - def deployment_EC_NETs_2(model, i): - if model.TIME['EC_NETs_2'][i] == 'NO': - return model.H[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_29 = pyo.Constraint(i, rule = deployment_EC_NETs_2) - - - #Technology implementation time for EC-NETs technology 3 - def deployment_EC_NETs_3(model, i): - if model.TIME['EC_NETs_3'][i] == 'NO': - return model.I[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_30 = pyo.Constraint(i, rule = deployment_EC_NETs_3) - - - #Technology implementation time for solar compensatory energy - def deployment_solar(model, i): - if model.TIME['SOLAR'][i] == 'NO': - return model.J[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_31 = pyo.Constraint(i, rule = deployment_solar) - - - #Technology implementation time for hydropower compensatory energy - def deployment_hydro(model, i): - if model.TIME['HYDRO'][i] == 'NO': - return model.K[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_32 = pyo.Constraint(i, rule = deployment_hydro) - - - #Technology implementation time for biomass compensatory energy - def deployment_biomass(model, i): - if model.TIME['BIOMASS'][i] == 'NO': - return model.L[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_33 = pyo.Constraint(i, rule = deployment_biomass) - - - #Technology implementation time for biogas compensatory energy - def deployment_biogas(model, i): - if model.TIME['BIOGAS'][i] == 'NO': - return model.M[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_34 = pyo.Constraint(i, rule = deployment_biogas) - - - #Technology implementation time for MSW compensatory energy - def deployment_MSW(model, i): - if model.TIME['MSW'][i] == 'NO': - return model.N[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_35 = pyo.Constraint(i, rule = deployment_MSW) - - - #Technology implementation time for alternative solid fuel type 1 - def deployment_alt_solid_1(model, i, s): - if model.TIME['SOLID_1'][i] == 'NO': - return model.O[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_36 = pyo.Constraint(i, model.S, rule = deployment_alt_solid_1) - - - #Technology implementation time for alternative solid fuel type 2 - def deployment_alt_solid_2(model, i, s): - if model.TIME['SOLID_2'][i] == 'NO': - return model.P[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_37 = pyo.Constraint(i, model.S, rule = deployment_alt_solid_2) - - - #Technology implementation time for alternative gas fuel type 1 - def deployment_alt_gas_1(model, i, s): - if model.TIME['GAS_1'][i] == 'NO': - return model.Q[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_38 = pyo.Constraint(i, model.S, rule = deployment_alt_gas_1) - - - #Technology implementation time for alternative gas fuel type 2 - def deployment_alt_gas_2(model, i, s): - if model.TIME['GAS_2'][i] == 'NO': - return model.R[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_39 = pyo.Constraint(i, model.S, rule = deployment_alt_gas_2) - - - #Big M formulation for EP-NETs technology 1 deployment for period i - def big_M_EP_NETs_1(model, i): - return model.EP_NET_1[i] <= model.D[i] * 1000 - - model.Cons_40 = pyo.Constraint(i, rule = big_M_EP_NETs_1) - - - #Big M formulation for EP-NETs technology 2 deployment for period i - def big_M_EP_NETs_2(model, i): - return model.EP_NET_2[i] <= model.E[i] * 1000 - - model.Cons_41 = pyo.Constraint(i, rule = big_M_EP_NETs_2) - - - #Big M formulation for EP-NETs technology 3 deployment for period i - def big_M_EP_NETs_3(model, i): - return model.EP_NET_3[i] <= model.F[i] * 1000 - - model.Cons_42 = pyo.Constraint(i, rule = big_M_EP_NETs_3) - - - #Big M formulation for EC-NETs technology 1 deployment for period i - def big_M_EC_NETs_1(model, i): - return model.EC_NET_1[i] <= model.G[i] * 1000 - - model.Cons_43 = pyo.Constraint(i, rule = big_M_EC_NETs_1) - - - #Big M formulation for EC-NETs technology 2 deployment for period i - def big_M_EC_NETs_2(model, i): - return model.EC_NET_2[i] <= model.H[i] * 1000 - - model.Cons_44 = pyo.Constraint(i, rule = big_M_EC_NETs_2) - - - #Big M formulation for EC-NETs technology 3 deployment for period i - def big_M_EC_NETs_3(model, i): - return model.EC_NET_3[i] <= model.I[i] * 1000 - - model.Cons_45 = pyo.Constraint(i, rule = big_M_EC_NETs_3) - - - #Big M formulation for deployment of solar renewable energy for period i - def big_M_solar(model, i): - return model.REN_SOLAR[i] <= model.J[i] * 1000 - - model.Cons_46 = pyo.Constraint(i, rule = big_M_solar) - - - #Big M formulation for deployment of hydro renewable energy for period i - def big_M_hydro(model, i): - return model.REN_HYDRO[i] <= model.K[i] * 1000 - - model.Cons_47 = pyo.Constraint(i, rule = big_M_hydro) - - - #Big M formulation for deployment of BM renewable energy for period i - def big_M_biomass(model, i): - return model.REN_BM[i] <= model.L[i] * 1000 - - model.Cons_48 = pyo.Constraint(i, rule = big_M_biomass) - - - #Big M formulation for deployment of BG renewable energy for period i - def big_M_biogas(model, i): - return model.REN_BG[i] <= model.M[i] * 1000 - - model.Cons_49 = pyo.Constraint(i, rule = big_M_biogas) - - - #Big M formulation for deployment of MSW renewable energy for period i - def big_M_MSW(model, i): - return model.REN_MSW[i] <= model.N[i] * 1000 - - model.Cons_50 = pyo.Constraint(i, rule = big_M_MSW) - - - #Big M formulation for deployment of alternative solid fuel type 1 for period i - def big_M_alt_solid_1(model, i, s): - return model.solid_1[i,s] <= model.O[i,s] * model.plant[s]['UB'] - - model.Cons_51 = pyo.Constraint(i, model.S, rule = big_M_alt_solid_1) - - - #Big M formulation for deployment of alternative solid fuel type 2 for period i - def big_M_alt_solid_2(model, i, s): - return model.solid_2[i,s] <= model.P[i,s] * model.plant[s]['UB'] - - model.Cons_52 = pyo.Constraint(i, model.S, rule = big_M_alt_solid_2) - - - #Big M formulation for deployment of alternative gas fuel type 1 for period i - def big_M_alt_gas_1(model, i, s): - return model.gas_1[i,s] <= model.Q[i,s] * model.plant[s]['UB'] - - model.Cons_53 = pyo.Constraint(i, model.S, rule = big_M_alt_gas_1) - - - #Big M formulation for deployment of alternative gas fuel type 2 for period i - def big_M_alt_gas_2(model, i, s): - return model.gas_2[i,s] <= model.R[i,s] * model.plant[s]['UB'] - - model.Cons_54 = pyo.Constraint(i, model.S, rule = big_M_alt_gas_2) - - - #Big M formulation for deployment of electricity for period i - def big_M_electricity(model, i, s): - return model.electricity[i] <= model.T[i] * 1000 - - model.Cons_E = pyo.Constraint(i, model.S, rule = big_M_electricity) - - - #The deployment of solar renewable energy at later periods should at least match the deployment in the previous period - def solar_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_SOLAR[i+1] >= model.REN_SOLAR[i] - - model.Cons_55 = pyo.Constraint(i, model.S, rule = solar_time_constraint) - - - #The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period - def hydro_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_HYDRO[i+1] >= model.REN_HYDRO[i] - - model.Cons_56 = pyo.Constraint(i, model.S, rule = hydro_time_constraint) - - - #The deployment of solar renewable energy at later periods should at least match the deployment in the previous period - def biomass_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_BM[i+1] >= model.REN_BM[i] - - model.Cons_57 = pyo.Constraint(i, model.S, rule = biomass_time_constraint) - - -#The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period - def biogas_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_BG[i+1] >= model.REN_BG[i] - - model.Cons_58 = pyo.Constraint(i, model.S, rule = biogas_time_constraint) - - - #The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period - def MSW_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_MSW[i+1] >= model.REN_MSW[i] - - model.Cons_59 = pyo.Constraint(i, model.S, rule = MSW_time_constraint) - - - #The deployment of EP-NETs technology 1 at later periods should at least match the deployment in the previous period - def EP_NETs_1_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EP_NET_1[i+1] >= model.EP_NET_1[i] - - model.Cons_60 = pyo.Constraint(i, model.S, rule = EP_NETs_1_time_constraint) - - - #The deployment of EP-NETs technology 2 at later periods should at least match the deployment in the previous period - def EP_NETs_2_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EP_NET_2[i+1] >= model.EP_NET_2[i] - - model.Cons_61 = pyo.Constraint(i, model.S, rule = EP_NETs_2_time_constraint) - - - #The deployment of EP-NETs technology 3 at later periods should at least match the deployment in the previous period - def EP_NETs_3_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EP_NET_3[i+1] >= model.EP_NET_3[i] - - model.Cons_62 = pyo.Constraint(i, model.S, rule = EP_NETs_3_time_constraint) - - - #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period - def EC_NETs_1_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EC_NET_1[i+1] >= model.EC_NET_1[i] - - model.Cons_63 = pyo.Constraint(i, model.S, rule = EC_NETs_1_time_constraint) - - - #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period - def EC_NETs_2_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EC_NET_2[i+1] >= model.EC_NET_2[i] - - model.Cons_64 = pyo.Constraint(i, model.S, rule = EC_NETs_2_time_constraint) - - - #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period - def EC_NETs_3_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EC_NET_3[i+1] >= model.EC_NET_3[i] - - model.Cons_65 = pyo.Constraint(i, model.S, rule = EC_NETs_3_time_constraint) - - - #Total energy contribution from all energy sources to satisfy the total demand for period i - def total_energy(model, i, s): - return sum((model.net_energy[i,s] + model.net_energy_CCS_1[i,s] + model.net_energy_CCS_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] + model.gas_1[i,s] + model.gas_2[i,s]) for s in model.S) + model.REN_SOLAR[i] + model.REN_HYDRO[i] + model.REN_BM[i] + model.REN_BG[i] + model.REN_MSW[i] + model.EP_NET_1[i] + model.EP_NET_2[i] + model.EP_NET_3[i] + model.electricity[i] == model.EP['Thermal'][i] + model.EP['Power'][i] + model.EC_NET_1[i] + model.EC_NET_2[i] + model.EC_NET_3[i] - - model.Cons_66 = pyo.Constraint(i, model.S, rule = total_energy) - - - #The total CO2 load contribution from all energy sources must satisfy most the CO2 emission limit in period i - def total_CO2_load(model, i, s): - return (sum((model.net_energy[i,s] * model.plant[s]['CI']) + (model.net_energy_CCS_1[i,s] * model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i])) + (model.net_energy_CCS_2[i,s] * model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i])) - + (model.solid_1[i,s] * model.SLD_CI['SOLID_1'][i]) + (model.solid_2[i,s] * model.SLD_CI['SOLID_2'][i]) - + (model.gas_1[i,s] * model.GAS_CI['GAS_1'][i]) + (model.gas_2[i,s] * model.GAS_CI['GAS_2'][i]) for s in model.S) - + (model.EC_NET_1[i] * model.NET_CI['EC_NETs_1'][i]) - + (model.EC_NET_2[i] * model.NET_CI['EC_NETs_2'][i]) - + (model.EC_NET_3[i] * model.NET_CI['EC_NETs_3'][i]) - + (model.EP_NET_1[i] * model.NET_CI['EP_NETs_1'][i]) - + (model.EP_NET_2[i] * model.NET_CI['EP_NETs_2'][i]) - + (model.EP_NET_3[i] * model.NET_CI['EP_NETs_3'][i]) - + (model.REN_SOLAR[i] * model.REN_CI['SOLAR'][i]) - + (model.REN_HYDRO[i] * model.REN_CI['HYDRO'][i]) - + (model.REN_BM[i] * model.REN_CI['BIOMASS'][i]) - + (model.REN_BG[i] * model.REN_CI['BIOGAS'][i]) - + (model.REN_MSW[i] * model.REN_CI['MSW'][i]) - + (model.electricity[i] * model.REN_CI['ELECTRICITY'][i]) == model.new_emission[i]) - - model.Cons_67 = pyo.Constraint(i, model.S, rule = total_CO2_load) - - - #Determining the cumulative total fuel and annualised capital cost for all power plants for period i - def energy_cost(model, i, s): - if 'SOLAR' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['SOLAR'][i]) - elif 'HYDRO' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['HYDRO'][i]) - elif 'BIOGAS' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOGAS'][i]) - elif 'BIOMASS' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOMASS'][i]) - elif 'MSW' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['MSW'][i]) - elif 'NG' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['NG'][i]) + (AFF * model.CPX_1['NG'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['NG'][i]) - elif 'FUEL OIL' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['FUEL OIL'][i]) + (AFF * model.CPX_1['FUEL OIL'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['FUEL OIL'][i]) - else: - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['COAL'][i]) + (AFF * model.CPX_1['COAL'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['COAL'][i]) - - model.Cons_68 = pyo.Constraint(i, model.S, rule = energy_cost) - - - #The summation of cost for each power plant s should equal to the total cost of each period i - def sum_cost(model, i, s): - return (sum((model.net_energy_CCS_1[i,s] * model.CCS_data['Cost_CCS_1'][i]) + (model.net_energy_CCS_2[i,s] * model.CCS_data['Cost_CCS_2'][i]) - + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]) - + (model.solid_1[i,s] * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]) + (model.solid_2[i,s] * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]) - + (model.gas_1[i,s] * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]) + (model.gas_2[i,s] * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]) for s in model.S) - + (model.EC_NET_1[i] * model.NET_COST['EC_NETs_1'][i]) + (AFF * model.CPX_1['EC_NETs_1'][i] * model.G[i]) + (AFF * model.EC_NET_1[i] * model.CPX_2['EC_NETs_1'][i]) - + (model.EC_NET_2[i] * model.NET_COST['EC_NETs_2'][i]) + (AFF * model.CPX_1['EC_NETs_2'][i] * model.H[i]) + (AFF * model.EC_NET_2[i] * model.CPX_2['EC_NETs_2'][i]) - + (model.EC_NET_3[i] * model.NET_COST['EC_NETs_3'][i]) + (AFF * model.CPX_1['EC_NETs_3'][i] * model.I[i]) + (AFF * model.EC_NET_3[i] * model.CPX_2['EC_NETs_3'][i]) - + (model.EP_NET_1[i] * model.NET_COST['EP_NETs_1'][i]) + (AFF * model.CPX_1['EP_NETs_1'][i] * model.D[i]) + (AFF * model.EP_NET_1[i] * model.CPX_2['EP_NETs_1'][i]) - + (model.EP_NET_2[i] * model.NET_COST['EP_NETs_2'][i]) + (AFF * model.CPX_1['EP_NETs_2'][i] * model.E[i]) + (AFF * model.EP_NET_2[i] * model.CPX_2['EP_NETs_2'][i]) - + (model.EP_NET_3[i] * model.NET_COST['EP_NETs_3'][i]) + (AFF * model.CPX_1['EP_NETs_3'][i] * model.F[i]) + (AFF * model.EP_NET_3[i] * model.CPX_2['EP_NETs_3'][i]) - + (model.REN_SOLAR[i] * model.REN_COST['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.J[i]) + (AFF * model.REN_SOLAR[i] * model.CPX_2['SOLAR'][i]) - + (model.REN_HYDRO[i] * model.REN_COST['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.K[i]) + (AFF * model.REN_HYDRO[i] * model.CPX_2['HYDRO'][i]) - + (model.REN_BM[i] * model.REN_COST['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.L[i]) + (AFF * model.REN_BM[i] * model.CPX_2['BIOMASS'][i]) - + (model.REN_BG[i] * model.REN_COST['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.M[i]) + (AFF * model.REN_BG[i] * model.CPX_2['BIOGAS'][i]) - + (model.REN_MSW[i] * model.REN_COST['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.N[i]) + (AFF * model.REN_MSW[i] * model.CPX_2['MSW'][i]) - + (model.electricity[i] * model.REN_COST['ELECTRICITY'][i]) + sum(model.energy_cost[i,s] for s in model.S) == model.sum_cost[i]) - - model.Cons_69 = pyo.Constraint(i, model.S, rule = sum_cost) - - - #For the minimum budget objective function, the total cost is minimised, subject to the satisfaction of the CO2 emission limit - #For the minimum emission objective function, the total emission is minimised, subject to the available budgetary constraint - def objective_constraint(model, i): - if flag == 'min_budget': - return model.new_emission[i] <= model.EP['Limit'][i] - else: - return model.sum_cost[i] <= model.EP['Budget'][i] - - model.Cons_70 = pyo.Constraint(i, rule = objective_constraint) - - - #opt = SolverFactory('octeract-engine', tee = True) - #results = opt.solve(model) - - opt = SolverFactory('gams') - #sys.exit() - results = opt.solve(model, solver = 'cplex') - - #opt = SolverFactory('gurobi', solver_io = 'python') - #results = opt.solve(model) - - print(results) - #model.pprint() - return model - -def multiperiod_energy_planning_results(model, i): - energy_planning = pd.DataFrame() - - for s in model.plant.keys(): - energy_planning.loc[s, 'Fuel'] = model.plant[s]['Fuel'] - energy_planning.loc[s, 'Energy Generation'] = model.A[i,s]() - energy_planning.loc[s, 'Gross Energy (TWh/y)'] = round(model.energy[i,s](), 2) - energy_planning.loc[s, 'CO2 Intensity (Mt/TWh)'] = model.plant[s]['CI'] - #energy_planning.loc[s, 'CCS_1 CI'] = round(model.CI_RET_1[i,s](), 3) - #energy_planning.loc[s, 'CCS_2 CI'] = round(model.CI_RET_2[i,s](), 3) - energy_planning.loc[s, 'CCS_1 Selection'] = model.B[i,s]() - energy_planning.loc[s, 'CCS_1 Ret (TWh/y)'] = round(model.CCS_1[i,s](), 2) - #energy_planning.loc[s, 'CCS_2 Selection'] = model.C[i,s]() - #energy_planning.loc[s, 'CCS_2 Ret (TWh/y)'] = round(model.CCS_2[i,s](), 2) - #energy_planning.loc[s, 'Net Energy wo CCS'] = round(model.net_energy[i,s](), 2) - #energy_planning.loc[s, 'Net Energy w CCS_1'] = round(model.net_energy_CCS_1[i,s](), 2) - #energy_planning.loc[s, 'Net Energy w CCS_2'] = round(model.net_energy_CCS_2[i,s](), 2) - #energy_planning.loc[s, 'Solid_1 Selection'] = model.O[i,s]() - #energy_planning.loc[s, 'SOLID_1 (TWh/y)'] = round(model.solid_1[i,s](), 2) - #energy_planning.loc[s, 'Solid_2 Selection'] = model.P[i,s]() - #energy_planning.loc[s, 'SOLID_2 (TWh/y)'] = round(model.solid_2[i,s](), 2) - #energy_planning.loc[s, 'Gas_1 Selection'] = model.Q[i,s]() - #energy_planning.loc[s, 'GAS_1 (TWh/y)'] = round(model.gas_1[i,s](), 2) - energy_planning.loc[s, 'Gas_2 Selection'] = model.R[i,s]() - energy_planning.loc[s, 'GAS_2 (TWh/y)'] = round(model.gas_2[i,s](), 2) - energy_planning.loc[s, 'Net Energy (TWh/y)'] = round(model.net_energy[i,s]() + model.net_energy_CCS_1[i,s]() + model.net_energy_CCS_2[i,s]() + model.solid_1[i,s]() + model.solid_2[i,s]() + model.gas_1[i,s]() + model.gas_2[i,s](), 2) - energy_planning.loc[s, 'CO2 Load (Mt/y)'] = round((model.net_energy[i,s]() * model.plant[s]['CI']) + (model.net_energy_CCS_1[i,s]() * model.CI_RET_1[i,s]()) + (model.net_energy_CCS_2[i,s]() * model.CI_RET_2[i,s]()) + (model.solid_1[i,s]() * model.SLD_CI['SOLID_1'][i]) + (model.solid_2[i,s]() * model.SLD_CI['SOLID_2'][i]) + (model.gas_1[i,s]() * model.GAS_CI['GAS_1'][i]) + (model.gas_2[i,s]() * model.GAS_CI['GAS_2'][i]), 2) - - #energy_planning.loc['EP_NET_1', 'Fuel'] = 'EP_NET_1' - #energy_planning.loc['EP_NET_2', 'Fuel'] = 'EP_NET_2' - energy_planning.loc['EP_NET_3', 'Fuel'] = 'EP_NET_3' - #energy_planning.loc['EC_NET_1', 'Fuel'] = 'EC_NET_1' - #energy_planning.loc['EC_NET_2', 'Fuel'] = 'EC_NET_2' - #energy_planning.loc['EC_NET_3', 'Fuel'] = 'EC_NET_3' - #energy_planning.loc['EP_NET_1', 'Energy Generation'] = model.D[i]() - #energy_planning.loc['EP_NET_2', 'Energy Generation'] = model.E[i]() - energy_planning.loc['EP_NET_3', 'Energy Generation'] = model.F[i]() - #energy_planning.loc['EC_NET_1', 'Energy Generation'] = model.G[i]() - #energy_planning.loc['EC_NET_2', 'Energy Generation'] = model.H[i]() - #energy_planning.loc['EC_NET_3', 'Energy Generation'] = model.I[i]() - #energy_planning.loc['EP_NET_1', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_1'][i] - #energy_planning.loc['EP_NET_2', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_2'][i] - energy_planning.loc['EP_NET_3', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_3'][i] - #energy_planning.loc['EC_NET_1', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_1'][i] - #energy_planning.loc['EC_NET_2', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_2'][i] - #energy_planning.loc['EC_NET_3', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_3'][i] - #energy_planning.loc['EP_NET_1', 'Net Energy (TWh/y)'] = round(model.EP_NET_1[i](), 2) - #energy_planning.loc['EP_NET_2', 'Net Energy (TWh/y)'] = round(model.EP_NET_2[i](), 2) - energy_planning.loc['EP_NET_3', 'Net Energy (TWh/y)'] = round(model.EP_NET_3[i](), 2) - #energy_planning.loc['EC_NET_1', 'Net Energy (TWh/y)'] = round(model.EC_NET_1[i](), 2) - #energy_planning.loc['EC_NET_2', 'Net Energy (TWh/y)'] = round(model.EC_NET_2[i](), 2) - #energy_planning.loc['EC_NET_3', 'Net Energy (TWh/y)'] = round(model.EC_NET_3[i](), 2) - #energy_planning.loc['EP_NET_1', 'CO2 Load (Mt/y)'] = round(model.EP_NET_1[i]() * model.NET_CI['EP_NETs_1'][i], 2) - #energy_planning.loc['EP_NET_2', 'CO2 Load (Mt/y)'] = round(model.EP_NET_2[i]() * model.NET_CI['EP_NETs_2'][i], 2) - energy_planning.loc['EP_NET_3', 'CO2 Load (Mt/y)'] = round(model.EP_NET_3[i]() * model.NET_CI['EP_NETs_3'][i], 2) - #energy_planning.loc['EC_NET_1', 'CO2 Load (Mt/y)'] = round(model.EC_NET_1[i]() * model.NET_CI['EC_NETs_1'][i], 2) - #energy_planning.loc['EC_NET_2', 'CO2 Load (Mt/y)'] = round(model.EC_NET_2[i]() * model.NET_CI['EC_NETs_2'][i], 2) - #energy_planning.loc['EC_NET_3', 'CO2 Load (Mt/y)'] = round(model.EC_NET_3[i]() * model.NET_CI['EC_NETs_3'][i], 2) - energy_planning.loc['ELECTRICITY', 'Fuel'] = 'ELECTRICITY' - energy_planning.loc['SOLAR', 'Fuel'] = 'SOLAR' - #energy_planning.loc['HYDRO', 'Fuel'] = 'HYDRO' - energy_planning.loc['BIOMASS', 'Fuel'] = 'BIOMASS' - energy_planning.loc['BIOGAS', 'Fuel'] = 'BIOGAS' - #energy_planning.loc['MSW', 'Fuel'] = 'MSW' - energy_planning.loc['ELECTRICITY', 'Energy Generation'] = model.T[i]() - energy_planning.loc['SOLAR', 'Energy Generation'] = model.J[i]() - #energy_planning.loc['HYDRO', 'Energy Generation'] = model.K[i]() - energy_planning.loc['BIOMASS', 'Energy Generation'] = model.L[i]() - energy_planning.loc['BIOGAS', 'Energy Generation'] = model.M[i]() - #energy_planning.loc['MSW', 'Energy Generation'] = model.N[i]() - energy_planning.loc['ELECTRICITY', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['ELECTRICITY'][i] - energy_planning.loc['SOLAR', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['SOLAR'][i] - #energy_planning.loc['HYDRO', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['HYDRO'][i] - energy_planning.loc['BIOMASS', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['BIOMASS'][i] - energy_planning.loc['BIOGAS', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['BIOGAS'][i] - #energy_planning.loc['MSW', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['MSW'][i] - energy_planning.loc['ELECTRICITY', 'Net Energy (TWh/y)'] = round(model.electricity[i](),2) - energy_planning.loc['ELECTRICITY', 'CO2 Load (Mt/y)'] = round(model.electricity[i]() * model.REN_CI['ELECTRICITY'][i], 2) - energy_planning.loc['SOLAR', 'Net Energy (TWh/y)'] = round(model.REN_SOLAR[i](),2) - energy_planning.loc['SOLAR', 'CO2 Load (Mt/y)'] = round(model.REN_SOLAR[i]() * model.REN_CI['SOLAR'][i], 2) - #energy_planning.loc['HYDRO', 'Net Energy (TWh/y)'] = round(model.REN_HYDRO[i](),2) - #energy_planning.loc['HYDRO', 'CO2 Load (Mt/y)'] = round(model.REN_HYDRO[i]() * model.REN_CI['HYDRO'][i], 2) - energy_planning.loc['BIOMASS', 'Net Energy (TWh/y)'] = round(model.REN_BM[i](),2) - energy_planning.loc['BIOMASS', 'CO2 Load (Mt/y)'] = round(model.REN_BM[i]() * model.REN_CI['BIOMASS'][i], 2) - energy_planning.loc['BIOGAS', 'Net Energy (TWh/y)'] = round(model.REN_BG[i](),2) - energy_planning.loc['BIOGAS', 'CO2 Load (Mt/y)'] = round(model.REN_BG[i]() * model.REN_CI['BIOGAS'][i], 2) - #energy_planning.loc['MSW', 'Net Energy (TWh/y)'] = round(model.REN_MSW[i](),2) - #energy_planning.loc['MSW', 'CO2 Load (Mt/y)'] = round(model.REN_MSW[i]() * model.REN_CI['MSW'][i], 2) - energy_planning.loc['TOTAL', 'CO2 Load (Mt/y)'] = round(model.new_emission[i](), 2) - energy_planning.loc['TOTAL', 'Total Cost (mil USD/y)'] = round(model.sum_cost[i](), 2) - - writer = pd.ExcelWriter(file_name, engine = 'openpyxl', mode = 'a', if_sheet_exists = 'new') - #writer = pd.ExcelWriter(file_name, mode = 'a', if_sheet_exists = 'new') - energy_planning.to_excel(writer, sheet_name = 'Results_Period_1') - writer.save() - - return - - -model = multiperiod_energy_planning(model, periods) - - -for i in list(range(1, numperiods,1)): - multiperiod_energy_planning_results(model,i) From 62686ab700011fa15a0b9e670a2a662ab8002e2c Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 00:34:39 +0500 Subject: [PATCH 04/24] Delete Industry_User_Interface.xlsx --- Industry_User_Interface.xlsx | Bin 54545 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 Industry_User_Interface.xlsx diff --git a/Industry_User_Interface.xlsx b/Industry_User_Interface.xlsx deleted file mode 100644 index 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z;|?%u(RDDka-^sG_4jSUthq3jUjlNX0M8P@?%RJHc!0&ze+No5u(h@P+g-2CTfLJ3 z+4csc#QsN8z@!`YcjABD*wxm-@_!J2%>;Px>CR6;2AE}lfH41qa|}@Y{9ClfzvTw; zpgL>>&{UlhzNG*GB>ewE`7M0%_JyDTHU3})+%;d}Ed`Y1uaw{N0B%0bZ*< z(p!oR(_bmSBMH2vOb!$c{4!zG0!$eH`Ch&NTI|0((eLdKZz(99u|VnIKtSjiZz+s? zf2I7kioB(8Ie1i70w^x%Zz-Tcf2I6h4SP$;Y7%`a1JwE5f5#F3Q`iUMf2I7^q<%|z z8;$%AWfV|qe|ayz)nDII-bMxeL;0flR|?~ACB?Uxw;}!hV9@pcj`{5|{TA~!8rL68 zw$a})f33c6qv-s>Fq;1z^Vj#IQIu*7Vvk>Ukm)LG}9l<=a9c+{#xK~6-@qM z@*@6@`D=l{_3{6M8H)XldF%22_N~12Qv3suNc|1?01Yne Date: Sun, 2 Apr 2023 00:34:54 +0500 Subject: [PATCH 05/24] Delete LICENSE --- LICENSE | 661 -------------------------------------------------------- 1 file changed, 661 deletions(-) delete mode 100644 LICENSE diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 0ad25db..0000000 --- a/LICENSE +++ /dev/null @@ -1,661 +0,0 @@ - GNU AFFERO GENERAL PUBLIC LICENSE - Version 3, 19 November 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and 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z^}YixKy#?06i!FTyx4`N|xYkTcj#xoHQYuvZABs+h+za%sS@AQ8Ez?%Q>pHg3V44qw4dg0lVhAk--8q%|8 u%AnNHkc$tbtk97E&+=jim-r|8F03lndzL@6v;bc{$r&qm5plj>`M&@iu)!bz From 0f0afa33d091696e0ad4a96768c5ff2b8217cb31 Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 00:35:19 +0500 Subject: [PATCH 07/24] Delete Optimal_Decarbonisation_Model_Python.py --- Optimal_Decarbonisation_Model_Python.py | 994 ------------------------ 1 file changed, 994 deletions(-) delete mode 100644 Optimal_Decarbonisation_Model_Python.py diff --git a/Optimal_Decarbonisation_Model_Python.py b/Optimal_Decarbonisation_Model_Python.py deleted file mode 100644 index a74243e..0000000 --- a/Optimal_Decarbonisation_Model_Python.py +++ /dev/null @@ -1,994 +0,0 @@ -''' -Created on 24th May 2022 - -Energy Planning Scenario in Pyomo - -This script presents the mathematical optimisation formulation concerning -carbon-constrained energy planning for a specific geographical region or -district. - -Power plants are fuelled by either renewable energy sources e.g., solar, -hydropower etc or fossil-based sources e.g., coal, natural gas etc. - -This energy planning is conducted for several periods (user-specified), with -each period having its respective demand and CO2 emission limit - -The satisfaction of the CO2 load limit is via the deployment of compensatory -energy, CCS technology, alternative low-carbon fuels (replacing fossil fuels), -and negative emission technologies (NETs) - -NETs are either made up of energy-producing NETs (EP-NETs) or energy-consuming -NETs (EC-NETs). Examples of EP-NETs are biomass and biochar, while EC-NETs -are made up of enhanced weathering, direct air capture etc. - -EP-NETs and EC-NETs produce and consume energy during CO2 removal respectively - -Alternative solid and gas fuels have low CO2 intensity, with a potential of -replacing coal and natural gas in fossil-based power plants respectively - -@author: Purusothmn, Dr Michael Short - -''' -import pyomo.environ as pyo -from pyomo.opt import SolverFactory -import pandas as pd -import os -from openpyxl import load_workbook - -cwd = os.getcwd() -model = pyo.ConcreteModel() - -file_name = r'Optimal_Decarbonisation_User_Interface_13.xlsx' -model.plant = pd.read_excel(file_name, sheet_name = 'PLANT_DATA', index_col = 0, header = 32, nrows = 7).to_dict() -model.EP = pd.read_excel(file_name, sheet_name = 'ENERGY_PLANNING_DATA', index_col = 0, header = 7).to_dict() -model.fuel = pd.read_excel(file_name, sheet_name = 'FUEL_COST_DATA', index_col = 0, header = 12).to_dict() -model.REN_CI = pd.read_excel(file_name, sheet_name = 'RENEWABLE_CI_DATA', index_col = 0, header = 9).to_dict() -model.REN_COST = pd.read_excel(file_name, sheet_name = 'RENEWABLE_COST_DATA', index_col = 0, header = 9).to_dict() -model.CPX_1 = pd.read_excel(file_name, sheet_name = 'CAPEX_DATA_1', index_col = 0, header = 20).to_dict() -model.CPX_2 = pd.read_excel(file_name, sheet_name = 'CAPEX_DATA_2', index_col = 0, header = 20).to_dict() -model.SLD_CI = pd.read_excel(file_name, sheet_name = 'ALT_SOLID_CI', index_col = 0, header = 6).to_dict() -model.SLD_COST = pd.read_excel(file_name, sheet_name = 'ALT_SOLID_COST', index_col = 0, header = 6).to_dict() -model.GAS_CI = pd.read_excel(file_name, sheet_name = 'ALT_GAS_CI', index_col = 0, header = 6).to_dict() -model.GAS_COST = pd.read_excel(file_name, sheet_name = 'ALT_GAS_COST', index_col = 0, header = 6).to_dict() -model.CCS_data = pd.read_excel(file_name, sheet_name = 'CCS_DATA', index_col = 0, header = 12).to_dict() -model.NET_CI = pd.read_excel(file_name, sheet_name = 'NET_CI_DATA', index_col = 0, header = 12).to_dict() -model.NET_COST = pd.read_excel(file_name, sheet_name = 'NET_COST_DATA', index_col = 0, header = 12).to_dict() -model.TIME = pd.read_excel(file_name, sheet_name = 'TECH_IMPLEMENTATION_TIME', index_col = 0, header = 19).to_dict() - -wb = load_workbook(file_name) - -sheet_1 = wb['PLANT_DATA'] -flag = sheet_1['B30'].value - -numperiods = sheet_1['B31'].value + 1 - -sheet_2 = wb['CAPEX_DATA_1'] -AFF = sheet_2['B18'].value - -periods = list(range(1,numperiods,1)) - -def multiperiod_energy_planning(model, i): - model.S = model.plant.keys() - - #LIST OF VARIABLES - #This variable determines the deployment of energy sources in power plant s for period i - model.energy = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the CO2 intensity of energy sources in power plant s with CCS technology 1 for period i - model.CI_RET_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the CO2 intensity of energy sources in power plant s with CCS technology 2 for period i - model.CI_RET_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #Binary variable for power generation by power plant s for period i - model.A = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of CCS technology 1 in power plant s for period i - model.B = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of CCS technology 2 in power plant s for period i - model.C = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of EP-NETs technology 1 for period i - model.D = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EP-NETs technology 2 for period i - model.E = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EP-NETs technology 3 for period i - model.F = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EC-NETs technology 1 for period i - model.G = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EC-NETs technology 2 for period i - model.H = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of EC-NETs technology 3 for period i - model.I = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of solar renewable energy for period i - model.J = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of hydropower renewable energy for period i - model.K = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of biomass renewable energy for period i - model.L = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of biogas renewable energy for period i - model.M = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of MSW renewable energy for period i - model.N = pyo.Var(i, domain = pyo.Binary) - - #Binary variable for the deployment of alternative solid-based fuel technology 1 in power plant s for period i - model.O = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of alternative solid-based fuel technology 2 in power plant s for period i - model.P = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of alternative gas-based fuel technology 1 in power plant s for period i - model.Q = pyo.Var(i, model.S, domain = pyo.Binary) - - #Binary variable for the deployment of alternative gas-based fuel technology 2 in power plant s for period i - model.R = pyo.Var(i, model.S, domain = pyo.Binary) - - #This variable represents the deployment of CCS technology 1 in power plant s for period i - model.CCS_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable represents the deployment of CCS technology 2 in power plant s for period i - model.CCS_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the net energy available from power plant s without CCS deployment for period i - model.net_energy = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the net energy available from power plant s with the deployment of CCS technology 1 for period i - model.net_energy_CCS_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the net energy available from power plant s with the deployment of CCS technology 2 for period i - model.net_energy_CCS_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EP_NETs technology 1 for period i - model.EP_NET_1 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EP_NETs technology 2 for period i - model.EP_NET_2 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EP_NETs technology 3 for period i - model.EP_NET_3 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EC_NETs technology 1 for period i - model.EC_NET_1 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EC_NETs technology 2 for period i - model.EC_NET_2 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable represents the minimum deployment of EC_NETs technology 3 for period i - model.EC_NET_3 = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of solar renewable energy for period i - model.REN_SOLAR = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of hydro renewable energy for period i - model.REN_HYDRO = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of biomass renewable energy for period i - model.REN_BM = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of biogas renewable energy for period i - model.REN_BG = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of MSW renewable energy for period i - model.REN_MSW = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of alternative solid-based fuel technology 1 for coal-based plant s for period i - model.solid_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of alternative solid-based fuel technology 2 for coal-based plant s for period i - model.solid_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of alternative gas-based fuel technology 1 for natural gas-based plant s for period i - model.gas_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the minimum deployment of alternative gas-based fuel technology 2 for natural gas-based plant s for period i - model.gas_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals) - - #This variable determines the revised total CO2 emissions for period i - model.new_emission = pyo.Var(i, domain = pyo.NonNegativeReals) - - #This variable determines the total energy cost of power plant s for period i - model.energy_cost = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) - - #This variable determines the total energy planning cost for period i - model.sum_cost = pyo.Var(i, domain = pyo.NonNegativeReals) - - - #OBJECTIVE FUNCTION - - #For the minimum budget objective function, the total cost is minimised, subject to the satisfaction of the CO2 emission limit - #For the minimum emission objective function, the total emission is minimised, subject to the available budgetary constraint - if flag == 'min_budget': - model.obj = pyo.Objective(expr = sum(model.sum_cost[i] for i in periods), sense = pyo.minimize) - else: - model.obj = pyo.Objective(expr = sum(model.new_emission[i] for i in periods), sense = pyo.minimize) - - #CONSTRAINTS - - - #Prior to any energy planning, the total power generation from all power plants should satisfy the regional energy demand for period i - def demand(model, i, s): - return model.EP['Demand'][i] == sum(model.energy[i,s] for s in model.S) - - model.Cons_1 = pyo.Constraint(i, model.S, rule = demand) - - - #The deployment of energy source in power plant s should at least satisfy the lower bound for period i - def lower_bound_energy(model, i, s): - return model.energy[i,s] >= model.plant[s]['LB'] * model.A[i,s] - - model.Cons_2 = pyo.Constraint(i, model.S, rule = lower_bound_energy) - - - #The deployment of energy source in power plant s should at most satisfy the upper bound for period i - def upper_bound_energy(model, i, s): - return model.energy[i,s] <= model.plant[s]['UB'] * model.A[i,s] - - model.Cons_3 = pyo.Constraint(i, model.S, rule = upper_bound_energy) - - - #There should not be power generation from power plant s before its commissioning period - def plant_commission(model, i, s): - if i < model.plant[s]['ON']: - return model.energy[i,s] == 0 - else: - return model.energy[i,s] >= 0 - - model.Cons_4 = pyo.Constraint(i, model.S, rule = plant_commission) - - - #There should be power generation from power plant s after its decommissioning period - def plant_decommission(model, i, s): - if i >= model.plant[s]['OFF']: - return model.energy[i,s] == 0 - else: - return model.energy[i,s] >= 0 - - model.Cons_5 = pyo.Constraint(i, model.S, rule = plant_decommission) - - - #If power plant s is decomissioned in a period, it should remain decommissioned at later periods - #However, power plant s should be available for power generation till the period before its decommissioning period - def energy_constraint(model, i, s): - if i <= model.plant[s]['OFF'] - 2: - return model.energy[i+1,s] >= model.energy[i,s] - else: - return pyo.Constraint.Skip - - model.Cons_6 = pyo.Constraint(i, model.S, rule = energy_constraint) - - - #Calculation of carbon intensity of energy sources with CCS technology 1 in power plant s for period i - def CCS_CI_1(model, i, s): - return model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i]) == model.CI_RET_1[i,s] - - model.Cons_7 = pyo.Constraint(i, model.S, rule = CCS_CI_1) - - - #Calculation of carbon intensity of energy sources with CCS technology 2 in power plant s for period i - def CCS_CI_2(model, i, s): - return model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i]) == model.CI_RET_2[i,s] - - model.Cons_8 = pyo.Constraint(i, model.S, rule = CCS_CI_2) - - - #If selected, the deployment of CCS technology 1 in power plant s is limited by the upper bound of the energy output for period i - def CCS_limit_1(model, i, s): - return model.CCS_1[i,s] <= model.plant[s]['UB'] * model.B[i,s] - - model.Cons_9 = pyo.Constraint(i, model.S, rule = CCS_limit_1) - - - #If selected, the deployment of CCS technology 2 in power plant s is limited by the upper bound of the energy output for period i - def CCS_limit_2(model, i, s): - return model.CCS_2[i,s] <= model.plant[s]['UB'] * model.C[i,s] - - model.Cons_10 = pyo.Constraint(i, model.S, rule = CCS_limit_2) - - - #The total CCS deployment in power plant s should be equal to summation of deployment of individual types of CCS technology for period i - def CCS_total(model, i, s): - return model.CCS_1[i,s] + model.CCS_2[i,s] <= model.energy[i,s] - - model.Cons_11 = pyo.Constraint(i, model.S, rule = CCS_total) - - - #The deployment of CCS technology 1 at later periods should at least match the deployment in the previous period - def CCS_1_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.CCS_1[i+1,s] >= model.CCS_1[i,s] - - model.Cons_12 = pyo.Constraint(i, model.S, rule = CCS_1_constraint) - - - #The deployment of CCS technology 2 at later periods should at least match the deployment in the previous period - def CCS_2_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.CCS_2[i+1,s] >= model.CCS_2[i,s] - - model.Cons_13 = pyo.Constraint(i, model.S, rule = CCS_2_constraint) - - - #Determine the net energy available from power plant s with CCS technology 1 for period i - def CCS_1_net_energy(model, i, s): - return model.CCS_1[i,s] * (1 - model.CCS_data['X_1'][i]) == model.net_energy_CCS_1[i,s] - - model.Cons_14 = pyo.Constraint(i, model.S, rule = CCS_1_net_energy) - - - #Determine the net energy available from power plant s with CCS technology 2 for period i - def CCS_2_net_energy(model, i, s): - return model.CCS_2[i,s] * (1 - model.CCS_data['X_2'][i]) == model.net_energy_CCS_2[i,s] - - model.Cons_15 = pyo.Constraint(i, model.S, rule = CCS_2_net_energy) - - - #The deployment of alternative solid fuel technology 1 at later periods should at least match the deployment in the previous period - def alt_solid_1_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.solid_1[i+1,s] >= model.solid_1[i,s] - - model.Cons_16 = pyo.Constraint(i, model.S, rule = alt_solid_1_constraint) - - - #The deployment of alternative solid fuel technology 2 at later periods should at least match the deployment in the previous period - def alt_solid_2_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.solid_2[i+1,s] >= model.solid_2[i,s] - - model.Cons_17 = pyo.Constraint(i, model.S, rule = alt_solid_2_constraint) - - - #The deployment of alternative gas fuel technology 1 at later periods should at least match the deployment in the previous period - def alt_gas_1_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.gas_1[i+1,s] >= model.gas_1[i,s] - - model.Cons_18 = pyo.Constraint(i, model.S, rule = alt_gas_1_constraint) - - - #The deployment of alternative gas fuel technology 2 at later periods should at least match the deployment in the previous period - def alt_gas_2_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.gas_2[i+1,s] >= model.gas_2[i,s] - - model.Cons_19 = pyo.Constraint(i, model.S, rule = alt_gas_2_constraint) - - - #The total energy contribution must equal the initially determined energy contribution of power plant s for period i - def fuel_substitution(model, i, s): - if 'REN' in model.plant[s].values(): - return model.net_energy[i,s] == model.energy[i,s] - elif 'NG' in model.plant[s].values(): - return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] + model.gas_1[i,s] + model.gas_2[i,s] == model.energy[i,s] - elif 'COAL' in model.plant[s].values(): - return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] == model.energy[i,s] - else: - return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] == model.energy[i,s] - - model.Cons_20 = pyo.Constraint(i, model.S, rule = fuel_substitution) - - - #If power plant s is fuelled by renewable energy sources, CCS technology 1 would not be deployed for period i - def no_CCS_1(model, i, s): - if 'REN' in model.plant[s].values(): - return model.CCS_1[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_21 = pyo.Constraint(i, model.S, rule = no_CCS_1) - - - #If power plant s is fuelled by renewable energy sources, CCS technology 1 would not be deployed for period i - def no_CCS_2(model, i, s): - if 'REN' in model.plant[s].values(): - return model.CCS_2[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_22 = pyo.Constraint(i, model.S, rule = no_CCS_2) - - - #Technology implementation time for CCS technology 1 - def deployment_CCS_1(model, i, s): - if model.TIME['CCS_1'][i] == 'NO': - return model.B[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_23 = pyo.Constraint(i, model.S, rule = deployment_CCS_1) - - - #Technology implementation time for CCS technology 2 - def deployment_CCS_2(model, i, s): - if model.TIME['CCS_2'][i] == 'NO': - return model.C[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_24 = pyo.Constraint(i, model.S, rule = deployment_CCS_2) - - - #Technology implementation time for EP-NETs technology 1 - def deployment_EP_NETs_1(model, i): - if model.TIME['EP_NETs_1'][i] == 'NO': - return model.D[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_25 = pyo.Constraint(i, rule = deployment_EP_NETs_1) - - - #Technology implementation time for EP-NETs technology 2 - def deployment_EP_NETs_2(model, i): - if model.TIME['EP_NETs_2'][i] == 'NO': - return model.E[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_26 = pyo.Constraint(i, rule = deployment_EP_NETs_2) - - - #Technology implementation time for EP-NETs technology 3 - def deployment_EP_NETs_3(model, i): - if model.TIME['EP_NETs_3'][i] == 'NO': - return model.F[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_27 = pyo.Constraint(i, rule = deployment_EP_NETs_3) - - - #Technology implementation time for EC-NETs technology 1 - def deployment_EC_NETs_1(model, i): - if model.TIME['EC_NETs_1'][i] == 'NO': - return model.G[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_28 = pyo.Constraint(i, rule = deployment_EC_NETs_1) - - - #Technology implementation time for EC-NETs technology 2 - def deployment_EC_NETs_2(model, i): - if model.TIME['EC_NETs_2'][i] == 'NO': - return model.H[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_29 = pyo.Constraint(i, rule = deployment_EC_NETs_2) - - - #Technology implementation time for EC-NETs technology 3 - def deployment_EC_NETs_3(model, i): - if model.TIME['EC_NETs_3'][i] == 'NO': - return model.I[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_30 = pyo.Constraint(i, rule = deployment_EC_NETs_3) - - - #Technology implementation time for solar compensatory energy - def deployment_solar(model, i): - if model.TIME['SOLAR'][i] == 'NO': - return model.J[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_31 = pyo.Constraint(i, rule = deployment_solar) - - - #Technology implementation time for hydropower compensatory energy - def deployment_hydro(model, i): - if model.TIME['HYDRO'][i] == 'NO': - return model.K[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_32 = pyo.Constraint(i, rule = deployment_hydro) - - - #Technology implementation time for biomass compensatory energy - def deployment_biomass(model, i): - if model.TIME['BIOMASS'][i] == 'NO': - return model.L[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_33 = pyo.Constraint(i, rule = deployment_biomass) - - - #Technology implementation time for biogas compensatory energy - def deployment_biogas(model, i): - if model.TIME['BIOGAS'][i] == 'NO': - return model.M[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_34 = pyo.Constraint(i, rule = deployment_biogas) - - - #Technology implementation time for MSW compensatory energy - def deployment_MSW(model, i): - if model.TIME['MSW'][i] == 'NO': - return model.N[i] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_35 = pyo.Constraint(i, rule = deployment_MSW) - - - #Technology implementation time for alternative solid fuel type 1 - def deployment_alt_solid_1(model, i, s): - if model.TIME['SOLID_1'][i] == 'NO': - return model.O[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_36 = pyo.Constraint(i, model.S, rule = deployment_alt_solid_1) - - - #Technology implementation time for alternative solid fuel type 2 - def deployment_alt_solid_2(model, i, s): - if model.TIME['SOLID_2'][i] == 'NO': - return model.P[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_37 = pyo.Constraint(i, model.S, rule = deployment_alt_solid_2) - - - #Technology implementation time for alternative gas fuel type 1 - def deployment_alt_gas_1(model, i, s): - if model.TIME['GAS_1'][i] == 'NO': - return model.Q[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_38 = pyo.Constraint(i, model.S, rule = deployment_alt_gas_1) - - - #Technology implementation time for alternative gas fuel type 2 - def deployment_alt_gas_2(model, i, s): - if model.TIME['GAS_2'][i] == 'NO': - return model.R[i,s] == 0 - else: - return pyo.Constraint.Skip - - model.Cons_39 = pyo.Constraint(i, model.S, rule = deployment_alt_gas_2) - - - #Big M formulation for EP-NETs technology 1 deployment for period i - def big_M_EP_NETs_1(model, i): - return model.EP_NET_1[i] <= model.D[i] * 1000 - - model.Cons_40 = pyo.Constraint(i, rule = big_M_EP_NETs_1) - - - #Big M formulation for EP-NETs technology 2 deployment for period i - def big_M_EP_NETs_2(model, i): - return model.EP_NET_2[i] <= model.E[i] * 1000 - - model.Cons_41 = pyo.Constraint(i, rule = big_M_EP_NETs_2) - - - #Big M formulation for EP-NETs technology 3 deployment for period i - def big_M_EP_NETs_3(model, i): - return model.EP_NET_3[i] <= model.F[i] * 1000 - - model.Cons_42 = pyo.Constraint(i, rule = big_M_EP_NETs_3) - - - #Big M formulation for EC-NETs technology 1 deployment for period i - def big_M_EC_NETs_1(model, i): - return model.EC_NET_1[i] <= model.G[i] * 1000 - - model.Cons_43 = pyo.Constraint(i, rule = big_M_EC_NETs_1) - - - #Big M formulation for EC-NETs technology 2 deployment for period i - def big_M_EC_NETs_2(model, i): - return model.EC_NET_2[i] <= model.H[i] * 1000 - - model.Cons_44 = pyo.Constraint(i, rule = big_M_EC_NETs_2) - - - #Big M formulation for EC-NETs technology 3 deployment for period i - def big_M_EC_NETs_3(model, i): - return model.EC_NET_3[i] <= model.I[i] * 1000 - - model.Cons_45 = pyo.Constraint(i, rule = big_M_EC_NETs_3) - - - #Big M formulation for deployment of solar renewable energy for period i - def big_M_solar(model, i): - return model.REN_SOLAR[i] <= model.J[i] * 1000 - - model.Cons_46 = pyo.Constraint(i, rule = big_M_solar) - - - #Big M formulation for deployment of hydro renewable energy for period i - def big_M_hydro(model, i): - return model.REN_HYDRO[i] <= model.K[i] * 1000 - - model.Cons_47 = pyo.Constraint(i, rule = big_M_hydro) - - - #Big M formulation for deployment of BM renewable energy for period i - def big_M_biomass(model, i): - return model.REN_BM[i] <= model.L[i] * 1000 - - model.Cons_48 = pyo.Constraint(i, rule = big_M_biomass) - - - #Big M formulation for deployment of BG renewable energy for period i - def big_M_biogas(model, i): - return model.REN_BG[i] <= model.M[i] * 1000 - - model.Cons_49 = pyo.Constraint(i, rule = big_M_biogas) - - - #Big M formulation for deployment of MSW renewable energy for period i - def big_M_MSW(model, i): - return model.REN_MSW[i] <= model.N[i] * 1000 - - model.Cons_50 = pyo.Constraint(i, rule = big_M_MSW) - - - #Big M formulation for deployment of alternative solid fuel type 1 for period i - def big_M_alt_solid_1(model, i, s): - return model.solid_1[i,s] <= model.O[i,s] * model.plant[s]['UB'] - - model.Cons_51 = pyo.Constraint(i, model.S, rule = big_M_alt_solid_1) - - - #Big M formulation for deployment of alternative solid fuel type 2 for period i - def big_M_alt_solid_2(model, i, s): - return model.solid_2[i,s] <= model.P[i,s] * model.plant[s]['UB'] - - model.Cons_52 = pyo.Constraint(i, model.S, rule = big_M_alt_solid_2) - - - #Big M formulation for deployment of alternative gas fuel type 1 for period i - def big_M_alt_gas_1(model, i, s): - return model.gas_1[i,s] <= model.Q[i,s] * model.plant[s]['UB'] - - model.Cons_53 = pyo.Constraint(i, model.S, rule = big_M_alt_gas_1) - - - #Big M formulation for deployment of alternative gas fuel type 2 for period i - def big_M_alt_gas_2(model, i, s): - return model.gas_2[i,s] <= model.R[i,s] * model.plant[s]['UB'] - - model.Cons_54 = pyo.Constraint(i, model.S, rule = big_M_alt_gas_2) - - - #The deployment of solar renewable energy at later periods should at least match the deployment in the previous period - def solar_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_SOLAR[i+1] >= model.REN_SOLAR[i] - - model.Cons_55 = pyo.Constraint(i, model.S, rule = solar_time_constraint) - - - #The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period - def hydro_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_HYDRO[i+1] >= model.REN_HYDRO[i] - - model.Cons_56 = pyo.Constraint(i, model.S, rule = hydro_time_constraint) - - - #The deployment of solar renewable energy at later periods should at least match the deployment in the previous period - def biomass_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_BM[i+1] >= model.REN_BM[i] - - model.Cons_57 = pyo.Constraint(i, model.S, rule = biomass_time_constraint) - - -#The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period - def biogas_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_BG[i+1] >= model.REN_BG[i] - - model.Cons_58 = pyo.Constraint(i, model.S, rule = biogas_time_constraint) - - - #The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period - def MSW_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.REN_MSW[i+1] >= model.REN_MSW[i] - - model.Cons_59 = pyo.Constraint(i, model.S, rule = MSW_time_constraint) - - - #The deployment of EP-NETs technology 1 at later periods should at least match the deployment in the previous period - def EP_NETs_1_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EP_NET_1[i+1] >= model.EP_NET_1[i] - - model.Cons_60 = pyo.Constraint(i, model.S, rule = EP_NETs_1_time_constraint) - - - #The deployment of EP-NETs technology 2 at later periods should at least match the deployment in the previous period - def EP_NETs_2_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EP_NET_2[i+1] >= model.EP_NET_2[i] - - model.Cons_61 = pyo.Constraint(i, model.S, rule = EP_NETs_2_time_constraint) - - - #The deployment of EP-NETs technology 3 at later periods should at least match the deployment in the previous period - def EP_NETs_3_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EP_NET_3[i+1] >= model.EP_NET_3[i] - - model.Cons_62 = pyo.Constraint(i, model.S, rule = EP_NETs_3_time_constraint) - - - #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period - def EC_NETs_1_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EC_NET_1[i+1] >= model.EC_NET_1[i] - - model.Cons_63 = pyo.Constraint(i, model.S, rule = EC_NETs_1_time_constraint) - - - #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period - def EC_NETs_2_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EC_NET_2[i+1] >= model.EC_NET_2[i] - - model.Cons_64 = pyo.Constraint(i, model.S, rule = EC_NETs_2_time_constraint) - - - #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period - def EC_NETs_3_time_constraint(model, i, s): - if i == numperiods - 1: - return pyo.Constraint.Skip - else: - return model.EC_NET_3[i+1] >= model.EC_NET_3[i] - - model.Cons_65 = pyo.Constraint(i, model.S, rule = EC_NETs_3_time_constraint) - - - #Total energy contribution from all energy sources to satisfy the total demand for period i - def total_energy(model, i, s): - return sum((model.net_energy[i,s] + model.net_energy_CCS_1[i,s] + model.net_energy_CCS_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] + model.gas_1[i,s] + model.gas_2[i,s]) for s in model.S) + model.REN_SOLAR[i] + model.REN_HYDRO[i] + model.REN_BM[i] + model.REN_BG[i] + model.REN_MSW[i] + model.EP_NET_1[i] + model.EP_NET_2[i] + model.EP_NET_3[i] == model.EP['Demand'][i] + model.EC_NET_1[i] + model.EC_NET_2[i] + model.EC_NET_3[i] - - model.Cons_66 = pyo.Constraint(i, model.S, rule = total_energy) - - - #The total CO2 load contribution from all energy sources must satisfy most the CO2 emission limit in period i - def total_CO2_load(model, i, s): - return (sum((model.net_energy[i,s] * model.plant[s]['CI']) + (model.net_energy_CCS_1[i,s] * model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i])) + (model.net_energy_CCS_2[i,s] * model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i])) - + (model.solid_1[i,s] * model.SLD_CI['SOLID_1'][i]) + (model.solid_2[i,s] * model.SLD_CI['SOLID_2'][i]) - + (model.gas_1[i,s] * model.GAS_CI['GAS_1'][i]) + (model.gas_2[i,s] * model.GAS_CI['GAS_2'][i]) for s in model.S) - + (model.EC_NET_1[i] * model.NET_CI['EC_NETs_1'][i]) - + (model.EC_NET_2[i] * model.NET_CI['EC_NETs_2'][i]) - + (model.EC_NET_3[i] * model.NET_CI['EC_NETs_3'][i]) - + (model.EP_NET_1[i] * model.NET_CI['EP_NETs_1'][i]) - + (model.EP_NET_2[i] * model.NET_CI['EP_NETs_2'][i]) - + (model.EP_NET_3[i] * model.NET_CI['EP_NETs_3'][i]) - + (model.REN_SOLAR[i] * model.REN_CI['SOLAR'][i]) - + (model.REN_HYDRO[i] * model.REN_CI['HYDRO'][i]) - + (model.REN_BM[i] * model.REN_CI['BIOMASS'][i]) - + (model.REN_BG[i] * model.REN_CI['BIOGAS'][i]) - + (model.REN_MSW[i] * model.REN_CI['MSW'][i]) == model.new_emission[i]) - - model.Cons_67 = pyo.Constraint(i, model.S, rule = total_CO2_load) - - - #Determining the cumulative total fuel and annualised capital cost for all power plants for period i - def energy_cost(model, i, s): - if 'SOLAR' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['SOLAR'][i]) - elif 'HYDRO' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['HYDRO'][i]) - elif 'BIOGAS' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOGAS'][i]) - elif 'BIOMASS' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOMASS'][i]) - elif 'MSW' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['MSW'][i]) - elif 'NG' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['NG'][i]) + (AFF * model.CPX_1['NG'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['NG'][i]) - elif 'OIL' in model.plant[s].values(): - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['OIL'][i]) + (AFF * model.CPX_1['OIL'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['OIL'][i]) - else: - return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['COAL'][i]) + (AFF * model.CPX_1['COAL'][i] * model.A[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['COAL'][i]) - - model.Cons_68 = pyo.Constraint(i, model.S, rule = energy_cost) - - - #The summation of cost for each power plant s should equal to the total cost of each period i - def sum_cost(model, i, s): - return (sum((model.net_energy_CCS_1[i,s] * model.CCS_data['Cost_CCS_1'][i]) + (model.net_energy_CCS_2[i,s] * model.CCS_data['Cost_CCS_2'][i]) - + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]) - + (model.solid_1[i,s] * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]) + (model.solid_2[i,s] * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]) - + (model.gas_1[i,s] * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]) + (model.gas_2[i,s] * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]) for s in model.S) - + (model.EC_NET_1[i] * model.NET_COST['EC_NETs_1'][i]) + (AFF * model.CPX_1['EC_NETs_1'][i] * model.G[i]) + (AFF * model.EC_NET_1[i] * model.CPX_2['EC_NETs_1'][i]) - + (model.EC_NET_2[i] * model.NET_COST['EC_NETs_2'][i]) + (AFF * model.CPX_1['EC_NETs_2'][i] * model.H[i]) + (AFF * model.EC_NET_2[i] * model.CPX_2['EC_NETs_2'][i]) - + (model.EC_NET_3[i] * model.NET_COST['EC_NETs_3'][i]) + (AFF * model.CPX_1['EC_NETs_3'][i] * model.I[i]) + (AFF * model.EC_NET_3[i] * model.CPX_2['EC_NETs_3'][i]) - + (model.EP_NET_1[i] * model.NET_COST['EP_NETs_1'][i]) + (AFF * model.CPX_1['EP_NETs_1'][i] * model.D[i]) + (AFF * model.EP_NET_1[i] * model.CPX_2['EP_NETs_1'][i]) - + (model.EP_NET_2[i] * model.NET_COST['EP_NETs_2'][i]) + (AFF * model.CPX_1['EP_NETs_2'][i] * model.E[i]) + (AFF * model.EP_NET_2[i] * model.CPX_2['EP_NETs_2'][i]) - + (model.EP_NET_3[i] * model.NET_COST['EP_NETs_3'][i]) + (AFF * model.CPX_1['EP_NETs_3'][i] * model.F[i]) + (AFF * model.EP_NET_3[i] * model.CPX_2['EP_NETs_3'][i]) - + (model.REN_SOLAR[i] * model.REN_COST['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.J[i]) + (AFF * model.REN_SOLAR[i] * model.CPX_2['SOLAR'][i]) - + (model.REN_HYDRO[i] * model.REN_COST['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.K[i]) + (AFF * model.REN_HYDRO[i] * model.CPX_2['HYDRO'][i]) - + (model.REN_BM[i] * model.REN_COST['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.L[i]) + (AFF * model.REN_BM[i] * model.CPX_2['BIOMASS'][i]) - + (model.REN_BG[i] * model.REN_COST['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.M[i]) + (AFF * model.REN_BG[i] * model.CPX_2['BIOGAS'][i]) - + (model.REN_MSW[i] * model.REN_COST['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.N[i]) + (AFF * model.REN_MSW[i] * model.CPX_2['MSW'][i]) - + sum(model.energy_cost[i,s] for s in model.S) == model.sum_cost[i]) - - model.Cons_69 = pyo.Constraint(i, model.S, rule = sum_cost) - - - #For the minimum budget objective function, the total cost is minimised, subject to the satisfaction of the CO2 emission limit - #For the minimum emission objective function, the total emission is minimised, subject to the available budgetary constraint - def objective_constraint(model, i): - if flag == 'min_budget': - return model.new_emission[i] <= model.EP['Limit'][i] - else: - return model.sum_cost[i] <= model.EP['Budget'][i] - - model.Cons_70 = pyo.Constraint(i, rule = objective_constraint) - - - #opt = SolverFactory('octeract-engine', tee = True) - #results = opt.solve(model) - - opt = SolverFactory('gams') - #sys.exit() - results = opt.solve(model, solver = 'cplex') - - #opt = SolverFactory('gurobi', solver_io = 'python') - #results = opt.solve(model) - - print(results) - #model.pprint() - return model - -def multiperiod_energy_planning_results(model, i): - energy_planning = pd.DataFrame() - - for s in model.plant.keys(): - energy_planning.loc[s, 'Fuel'] = model.plant[s]['Fuel'] - energy_planning.loc[s, 'Energy Generation'] = model.A[i,s]() - energy_planning.loc[s, 'Gross Energy (TWh/y)'] = round(model.energy[i,s](), 2) - energy_planning.loc[s, 'CO2 Intensity (Mt/TWh)'] = model.plant[s]['CI'] - #energy_planning.loc[s, 'CCS_1 CI'] = round(model.CI_RET_1[i,s](), 3) - #energy_planning.loc[s, 'CCS_2 CI'] = round(model.CI_RET_2[i,s](), 3) - energy_planning.loc[s, 'CCS_1 Selection'] = model.B[i,s]() - energy_planning.loc[s, 'CCS_1 Ret (TWh/y)'] = round(model.CCS_1[i,s](), 2) - energy_planning.loc[s, 'CCS_2 Selection'] = model.C[i,s]() - energy_planning.loc[s, 'CCS_2 Ret (TWh/y)'] = round(model.CCS_2[i,s](), 2) - #energy_planning.loc[s, 'Net Energy wo CCS'] = round(model.net_energy[i,s](), 2) - #energy_planning.loc[s, 'Net Energy w CCS_1'] = round(model.net_energy_CCS_1[i,s](), 2) - #energy_planning.loc[s, 'Net Energy w CCS_2'] = round(model.net_energy_CCS_2[i,s](), 2) - energy_planning.loc[s, 'Solid_1 Selection'] = model.O[i,s]() - energy_planning.loc[s, 'SOLID_1 (TWh/y)'] = round(model.solid_1[i,s](), 2) - energy_planning.loc[s, 'Solid_2 Selection'] = model.P[i,s]() - energy_planning.loc[s, 'SOLID_2 (TWh/y)'] = round(model.solid_2[i,s](), 2) - energy_planning.loc[s, 'Gas_1 Selection'] = model.Q[i,s]() - energy_planning.loc[s, 'GAS_1 (TWh/y)'] = round(model.gas_1[i,s](), 2) - energy_planning.loc[s, 'Gas_2 Selection'] = model.R[i,s]() - energy_planning.loc[s, 'GAS_2 (TWh/y)'] = round(model.gas_2[i,s](), 2) - energy_planning.loc[s, 'Net Energy (TWh/y)'] = round(model.net_energy[i,s]() + model.net_energy_CCS_1[i,s]() + model.net_energy_CCS_2[i,s]() + model.solid_1[i,s]() + model.solid_2[i,s]() + model.gas_1[i,s]() + model.gas_2[i,s](), 2) - energy_planning.loc[s, 'CO2 Load (Mt/y)'] = round((model.net_energy[i,s]() * model.plant[s]['CI']) + (model.net_energy_CCS_1[i,s]() * model.CI_RET_1[i,s]()) + (model.net_energy_CCS_2[i,s]() * model.CI_RET_2[i,s]()) + (model.solid_1[i,s]() * model.SLD_CI['SOLID_1'][i]) + (model.solid_2[i,s]() * model.SLD_CI['SOLID_2'][i]) + (model.gas_1[i,s]() * model.GAS_CI['GAS_1'][i]) + (model.gas_2[i,s]() * model.GAS_CI['GAS_2'][i]), 2) - - energy_planning.loc['EP_NET_1', 'Fuel'] = 'EP_NET_1' - energy_planning.loc['EP_NET_2', 'Fuel'] = 'EP_NET_2' - energy_planning.loc['EP_NET_3', 'Fuel'] = 'EP_NET_3' - energy_planning.loc['EC_NET_1', 'Fuel'] = 'EC_NET_1' - energy_planning.loc['EC_NET_2', 'Fuel'] = 'EC_NET_2' - energy_planning.loc['EC_NET_3', 'Fuel'] = 'EC_NET_3' - energy_planning.loc['EP_NET_1', 'Energy Generation'] = model.D[i]() - energy_planning.loc['EP_NET_2', 'Energy Generation'] = model.E[i]() - energy_planning.loc['EP_NET_3', 'Energy Generation'] = model.F[i]() - energy_planning.loc['EC_NET_1', 'Energy Generation'] = model.G[i]() - energy_planning.loc['EC_NET_2', 'Energy Generation'] = model.H[i]() - energy_planning.loc['EC_NET_3', 'Energy Generation'] = model.I[i]() - energy_planning.loc['EP_NET_1', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_1'][i] - energy_planning.loc['EP_NET_2', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_2'][i] - energy_planning.loc['EP_NET_3', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_3'][i] - energy_planning.loc['EC_NET_1', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_1'][i] - energy_planning.loc['EC_NET_2', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_2'][i] - energy_planning.loc['EC_NET_3', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_3'][i] - energy_planning.loc['EP_NET_1', 'Net Energy (TWh/y)'] = round(model.EP_NET_1[i](), 2) - energy_planning.loc['EP_NET_2', 'Net Energy (TWh/y)'] = round(model.EP_NET_2[i](), 2) - energy_planning.loc['EP_NET_3', 'Net Energy (TWh/y)'] = round(model.EP_NET_3[i](), 2) - energy_planning.loc['EC_NET_1', 'Net Energy (TWh/y)'] = round(model.EC_NET_1[i](), 2) - energy_planning.loc['EC_NET_2', 'Net Energy (TWh/y)'] = round(model.EC_NET_2[i](), 2) - energy_planning.loc['EC_NET_3', 'Net Energy (TWh/y)'] = round(model.EC_NET_3[i](), 2) - energy_planning.loc['EP_NET_1', 'CO2 Load (Mt/y)'] = round(model.EP_NET_1[i]() * model.NET_CI['EP_NETs_1'][i], 2) - energy_planning.loc['EP_NET_2', 'CO2 Load (Mt/y)'] = round(model.EP_NET_2[i]() * model.NET_CI['EP_NETs_2'][i], 2) - energy_planning.loc['EP_NET_3', 'CO2 Load (Mt/y)'] = round(model.EP_NET_3[i]() * model.NET_CI['EP_NETs_3'][i], 2) - energy_planning.loc['EC_NET_1', 'CO2 Load (Mt/y)'] = round(model.EC_NET_1[i]() * model.NET_CI['EC_NETs_1'][i], 2) - energy_planning.loc['EC_NET_2', 'CO2 Load (Mt/y)'] = round(model.EC_NET_2[i]() * model.NET_CI['EC_NETs_2'][i], 2) - energy_planning.loc['EC_NET_3', 'CO2 Load (Mt/y)'] = round(model.EC_NET_3[i]() * model.NET_CI['EC_NETs_3'][i], 2) - energy_planning.loc['SOLAR', 'Fuel'] = 'SOLAR' - energy_planning.loc['HYDRO', 'Fuel'] = 'HYDRO' - energy_planning.loc['BIOMASS', 'Fuel'] = 'BIOMASS' - energy_planning.loc['BIOGAS', 'Fuel'] = 'BIOGAS' - energy_planning.loc['MSW', 'Fuel'] = 'MSW' - energy_planning.loc['SOLAR', 'Energy Generation'] = model.J[i]() - energy_planning.loc['HYDRO', 'Energy Generation'] = model.K[i]() - energy_planning.loc['BIOMASS', 'Energy Generation'] = model.L[i]() - energy_planning.loc['BIOGAS', 'Energy Generation'] = model.M[i]() - energy_planning.loc['MSW', 'Energy Generation'] = model.N[i]() - energy_planning.loc['SOLAR', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['SOLAR'][i] - energy_planning.loc['HYDRO', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['HYDRO'][i] - energy_planning.loc['BIOMASS', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['BIOMASS'][i] - energy_planning.loc['BIOGAS', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['BIOGAS'][i] - energy_planning.loc['MSW', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['MSW'][i] - energy_planning.loc['SOLAR', 'Net Energy (TWh/y)'] = round(model.REN_SOLAR[i](),2) - energy_planning.loc['SOLAR', 'CO2 Load (Mt/y)'] = round(model.REN_SOLAR[i]() * model.REN_CI['SOLAR'][i], 2) - energy_planning.loc['HYDRO', 'Net Energy (TWh/y)'] = round(model.REN_HYDRO[i](),2) - energy_planning.loc['HYDRO', 'CO2 Load (Mt/y)'] = round(model.REN_HYDRO[i]() * model.REN_CI['HYDRO'][i], 2) - energy_planning.loc['BIOMASS', 'Net Energy (TWh/y)'] = round(model.REN_BM[i](),2) - energy_planning.loc['BIOMASS', 'CO2 Load (Mt/y)'] = round(model.REN_BM[i]() * model.REN_CI['BIOMASS'][i], 2) - energy_planning.loc['BIOGAS', 'Net Energy (TWh/y)'] = round(model.REN_BG[i](),2) - energy_planning.loc['BIOGAS', 'CO2 Load (Mt/y)'] = round(model.REN_BG[i]() * model.REN_CI['BIOGAS'][i], 2) - energy_planning.loc['MSW', 'Net Energy (TWh/y)'] = round(model.REN_MSW[i](),2) - energy_planning.loc['MSW', 'CO2 Load (Mt/y)'] = round(model.REN_MSW[i]() * model.REN_CI['MSW'][i], 2) - energy_planning.loc['TOTAL', 'CO2 Load (Mt/y)'] = round(model.new_emission[i](), 2) - energy_planning.loc['TOTAL', 'Total Cost (mil USD/y)'] = round(model.sum_cost[i](), 2) - - writer = pd.ExcelWriter(file_name, engine = 'openpyxl', mode = 'a', if_sheet_exists = 'new') - #writer = pd.ExcelWriter(file_name, mode = 'a', if_sheet_exists = 'new') - energy_planning.to_excel(writer, sheet_name = 'Results_Period_1') - writer.save() - - return - - -model = multiperiod_energy_planning(model, periods) - -''' -for i in list(range(1, numperiods,1)): - multiperiod_energy_planning_results(model,i) -''' \ No newline at end of file From 3315ab1e368b9b55115632cacbaa91f49f79545c Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 00:35:28 +0500 Subject: [PATCH 08/24] Delete Optimal_Decarbonisation_Run_File.py --- Optimal_Decarbonisation_Run_File.py | 18 ------------------ 1 file changed, 18 deletions(-) delete mode 100644 Optimal_Decarbonisation_Run_File.py diff --git a/Optimal_Decarbonisation_Run_File.py b/Optimal_Decarbonisation_Run_File.py deleted file mode 100644 index f1744fe..0000000 --- a/Optimal_Decarbonisation_Run_File.py +++ /dev/null @@ -1,18 +0,0 @@ -''' -Created on 24th September 2021 - -This script presents the mathematical optimisation formulation for an energy planning scenario in a specific geographical region or district. -Renewable energy and fossil-based sources make up the power generation in a specific geographical region or district. -Each period consists of its respective energy demand and CO2 emission limit. -EP-NETs and EC-NETs may be utilised to achieve the CO2 emission limit for each period. -This energy planning mathematical formulation may be optimised subject to either a mininum cost or minimum emission objective function. - -@author: Purusothmn, Dr Michael Short, Prof Dr Dominic C.Y.F. -''' -import os - -path = os.chdir(r'COPY_PATHNAME_ AND_REPLACE_THIS_TEXT') - -cwd = os.getcwd() - -import Optimal_Decarbonisation_Model_Python \ No newline at end of file From 6106d6f881370b7cac9fbd27a2d7ce00d87f7183 Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 00:35:39 +0500 Subject: [PATCH 09/24] Delete Optimal_Decarbonisation_User_Interface.xlsx --- Optimal_Decarbonisation_User_Interface.xlsx | Bin 49198 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 Optimal_Decarbonisation_User_Interface.xlsx diff --git a/Optimal_Decarbonisation_User_Interface.xlsx b/Optimal_Decarbonisation_User_Interface.xlsx deleted file mode 100644 index 6aab4f88b91ea983d9fe6d3f5d9f29714f76a4b5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 49198 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z_2HKhlYcQb34bsz<0oHw%*&XMzkuWPKY+gxATLc{&dL2X4bA)4^yLiQOTx>p>0d%` z Date: Sun, 2 Apr 2023 00:35:51 +0500 Subject: [PATCH 10/24] Delete README.md --- README.md | 7 ------- 1 file changed, 7 deletions(-) delete mode 100644 README.md diff --git a/README.md b/README.md deleted file mode 100644 index 9cbcc68..0000000 --- a/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# DECO2 - -DECarbonisation Option Optimisation (DECO2) is an optimal decarbonisation framework developed as part of a British Council-funded Trilateral Research Initiative between University of Surrey, University of Nottingham Malaysia, and University of Tokyo, with project parters De La Sall University (Philippines), Monash University Malaysia, Malaysian Green Technology Centre and Aria Sustainability. - -The framework combines rigorous mathematical programming models (mixed-integer linear programming) to determine optimal pathways to decarbonisation through implementation of carbon apture and utilisation, negative emissions technologies, and alternative energy sources for optimal energy transitions. Please read the user manual in the main folder for information on the formulation and how the inputs work. For details on the partnership, please visit our site: - -https://www.surrey.ac.uk/research-projects/software-framework-optimal-decarbonisation-planning-asean-countries From 4b4455ab74ad2914b9a6f3da66431e01765837ff Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 00:36:02 +0500 Subject: [PATCH 11/24] Delete environment.yml --- environment.yml | 247 ------------------------------------------------ 1 file changed, 247 deletions(-) delete mode 100644 environment.yml diff --git a/environment.yml b/environment.yml deleted file mode 100644 index 0eba0cf..0000000 --- a/environment.yml +++ /dev/null @@ -1,247 +0,0 @@ -name: Pyomo_v1 -channels: - - gurobi - - conda-forge - - defaults -dependencies: - - alabaster=0.7.12=py_0 - - arrow=1.2.2=pyhd8ed1ab_0 - - astroid=2.11.6=py38haa244fe_0 - - atomicwrites=1.4.1=pyhd8ed1ab_0 - - attrs=22.1.0=pyh71513ae_1 - - autopep8=1.6.0=pyhd8ed1ab_1 - - babel=2.10.3=pyhd8ed1ab_0 - - 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spyder=5.3.2=py38haa244fe_0 - - spyder-kernels=2.3.2=py38haa244fe_0 - - sqlite=3.39.2=h8ffe710_0 - - tbb=2021.5.0=h2d74725_1 - - text-unidecode=1.3=py_0 - - textdistance=4.3.0=pyhd8ed1ab_0 - - three-merge=0.1.1=pyh9f0ad1d_0 - - tinycss2=1.1.1=pyhd8ed1ab_0 - - tk=8.6.12=h8ffe710_0 - - toml=0.10.2=pyhd8ed1ab_0 - - tomli=2.0.1=pyhd8ed1ab_0 - - tomlkit=0.11.1=pyha770c72_0 - - tornado=6.2=py38h294d835_0 - - traitlets=5.3.0=pyhd8ed1ab_0 - - typing=3.10.0.0=pyhd8ed1ab_0 - - typing-extensions=4.3.0=hd8ed1ab_0 - - typing_extensions=4.3.0=pyha770c72_0 - - ucrt=10.0.20348.0=h57928b3_0 - - ujson=5.3.0=py38h885f38d_0 - - unidecode=1.3.4=pyhd8ed1ab_0 - - urllib3=1.26.11=pyhd8ed1ab_0 - - vc=14.2=hb210afc_6 - - vs2015_runtime=14.29.30037=h902a5da_6 - - watchdog=2.1.9=py38haa244fe_0 - - wcwidth=0.2.5=pyh9f0ad1d_2 - - webencodings=0.5.1=py_1 - - whatthepatch=1.0.2=pyhd8ed1ab_0 - - wheel=0.37.1=pyhd8ed1ab_0 - - win_inet_pton=1.1.0=py38haa244fe_4 - - wrapt=1.14.1=py38h294d835_0 - - xz=5.2.5=h62dcd97_1 - - yaml=0.2.5=h8ffe710_2 - - yapf=0.32.0=pyhd8ed1ab_0 - - zeromq=4.3.4=h0e60522_1 - - zipp=3.8.1=pyhd8ed1ab_0 - - zlib=1.2.12=h8ffe710_2 - - zstd=1.5.2=h6255e5f_2 - - pip: - - cycler==0.10.0 - - et-xmlfile==1.1.0 - - gekko==1.0.0 - - kiwisolver==1.3.1 - - matplotlib==3.4.1 - - mpmath==1.2.1 - - networkx==2.5.1 - - openpyxl==3.0.7 - - pillow==8.2.0 - - pint==0.17 - - pymysql==1.0.2 - - pyodbc==4.0.30 - - pyro4==4.80 - - pyscipopt==3.2.0 - - serpent==1.30.2 - - sympy==1.8 - - xlrd==2.0.1 - - xlsxwriter==1.4.5 -prefix: C:\Users\LENOVO\anaconda3\envs\Pyomo_v1 From 350f395996126701e90c5bdbf7e657e452b939c5 Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 00:47:44 +0500 Subject: [PATCH 12/24] Add files via upload --- ...DO_EP_CT_Base_User_Interface_Pakistan.xlsx | Bin 0 -> 80398 bytes ...EP_Optimal_Decarbonisation_Model_Python.py | 1590 +++++++++++++++++ .../DO_EP_Optimal_Decarbonisation_Run_File.py | 20 + Direct Optimization Model/LICENSE | 661 +++++++ Direct Optimization Model/README.md | 7 + .../IDO_Base_Model_Python.cpython-38.pyc | Bin 0 -> 58687 bytes Direct Optimization Model/environment.yml | 247 +++ 7 files changed, 2525 insertions(+) create mode 100644 Direct Optimization Model/DO_EP_CT_Base_User_Interface_Pakistan.xlsx create mode 100644 Direct Optimization Model/DO_EP_Optimal_Decarbonisation_Model_Python.py create mode 100644 Direct Optimization Model/DO_EP_Optimal_Decarbonisation_Run_File.py create mode 100644 Direct Optimization Model/LICENSE create mode 100644 Direct Optimization Model/README.md create mode 100644 Direct Optimization Model/__pycache__/IDO_Base_Model_Python.cpython-38.pyc create mode 100644 Direct Optimization Model/environment.yml diff --git a/Direct Optimization Model/DO_EP_CT_Base_User_Interface_Pakistan.xlsx b/Direct Optimization Model/DO_EP_CT_Base_User_Interface_Pakistan.xlsx new file mode 100644 index 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+Last Modified in March 2023 + +Direct Optimization Energy Planning Scenario in Pyomo + +This script presents the mathematical optimisation formulation concerning +carbon-constrained energy planning for a specific geographical region or +district. + +Power plants are fuelled by either renewable energy sources e.g., solar, +hydropower, wind, etc. or fossil-based sources e.g., coal, natural gas, oil, nuclear, etc. + +This energy planning is conducted for several periods (user-specified), with +each period having its respective demand and CO2 emission limit + +The satisfaction of the CO2 load limit is via the deployment of compensatory +energy, CCS/CCSU technology, alternative low-carbon fuels (replacing fossil fuels), +negative emission technologies (NETs) and carbon trading. + +NETs are either made up of energy-producing NETs (EP-NETs) or energy-consuming +NETs (EC-NETs). Examples of EP-NETs are biomass and biochar, while EC-NETs +are made up of enhanced weathering, direct air capture etc. + +EP-NETs and EC-NETs produce and consume energy during CO2 removal respectively + +Alternative solid and gas fuels have low CO2 intensity, with a potential of +replacing coal and natural gas in fossil-based power plants respectively + +Carbon Trading adds a lot of dimensions which enable the formulation to reduce costs and emissions in an energy system +Concept of carbon is becoming crucial in controlling the emissions + +@author: Gul, Purusothmn, Dr Michael Short + +''' +import pyomo.environ as pyo +from pyomo.opt import SolverFactory +import pandas as pd +import os +from openpyxl import load_workbook + +path = os.chdir(r'C:\Users\pc\Desktop\DECO2 v2023\Direct Optimization Model') + +cwd = os.getcwd() + +model = pyo.ConcreteModel() + +file_name = r'DO_EP_CT_Base_User_Interface_Pakistan.xlsx' + +model.plant = pd.read_excel(file_name, sheet_name = 'PLANT_DATA', index_col = 0, header = 37, nrows = 7).to_dict() +model.EP = pd.read_excel(file_name, sheet_name = 'ENERGY_PLANNING_DATA', index_col = 0, header = 7).to_dict() +model.fuel = pd.read_excel(file_name, sheet_name = 'FUEL_COST_DATA', index_col = 0, header = 12).to_dict() +model.REN_CI = pd.read_excel(file_name, sheet_name = 'RENEWABLE_CI_DATA', index_col = 0, header = 9).to_dict() +model.REN_COST = pd.read_excel(file_name, sheet_name = 'RENEWABLE_COST_DATA', index_col = 0, header = 9).to_dict() +model.CPX_1 = pd.read_excel(file_name, sheet_name = 'CAPEX_DATA_1', index_col = 0, header = 20).to_dict() +model.CPX_2 = pd.read_excel(file_name, sheet_name = 'CAPEX_DATA_2', index_col = 0, header = 20).to_dict() +model.SLD_CI = pd.read_excel(file_name, sheet_name = 'ALT_SOLID_CI', index_col = 0, header = 6).to_dict() +model.SLD_COST = pd.read_excel(file_name, sheet_name = 'ALT_SOLID_COST', index_col = 0, header = 6).to_dict() +model.GAS_CI = pd.read_excel(file_name, sheet_name = 'ALT_GAS_CI', index_col = 0, header = 6).to_dict() +model.GAS_COST = pd.read_excel(file_name, sheet_name = 'ALT_GAS_COST', index_col = 0, header = 6).to_dict() +model.CCS_data = pd.read_excel(file_name, sheet_name = 'CCS_DATA', index_col = 0, header = 12).to_dict() +model.NET_CI = pd.read_excel(file_name, sheet_name = 'NET_CI_DATA', index_col = 0, header = 12).to_dict() +model.NET_COST = pd.read_excel(file_name, sheet_name = 'NET_COST_DATA', index_col = 0, header = 12).to_dict() +model.TIME = pd.read_excel(file_name, sheet_name = 'TECH_IMPLEMENTATION_TIME', index_col = 0, header = 19).to_dict() + +wb = load_workbook(file_name) + +sheet_1 = wb['PLANT_DATA'] + +#flag specifies if the problem is for maximizing profits, minimizing emissions or minimizing the budget +flag = sheet_1['B32'].value +#CT defines if the carbon trading is turned on or off +CT = sheet_1['G32'].value +#EB defines the emissions available initially to buy from previous period i = 1-1 = 0 +EB = sheet_1['G33'].value +#CT_Percentage_Emissions define the percentage of emissions participating in CT +CT_Percentage_Emissions = sheet_1['G34'].value +#Elec_Price_Inflation define the factor in percentage with which electricity price is to be increased as we move to the next period +Elec_Price_Inflation = sheet_1['G35'].value +#Initial_Elec_Price define the initial electricity price for period 1 +Initial_Elec_Price = sheet_1['B34'].value +#Max_Elec_Price define the maximum electricity price possible for the final period +Max_Elec_Price = sheet_1['B36'].value +#numperiods define the number of periods for which the optimization problem is solved +numperiods = sheet_1['B33'].value + 1 +#Plant_Profit defines if the plant positive profitability is turned on or off for the optimization study +Plant_Profit = sheet_1['B35'].value +#Print_Result indicates if results will be displayed in user interface or not +Print_Result = sheet_1['G36'].value + +sheet_2 = wb['CAPEX_DATA_1'] + +#AFF defines the annualized factor +AFF = sheet_2['B18'].value +#periods allocates the number to each period +periods = list(range(1,numperiods,1)) + + +def multiperiod_energy_planning(model, i): + model.S = model.plant.keys() + + #LIST OF VARIABLES + #This variable determines the deployment of energy sources in power plant s for period i + model.energy = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the CO2 intensity of energy sources in power plant s with CCS technology 1 for period i + model.CI_RET_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0.088) + + #This variable determines the CO2 intensity of energy sources in power plant s with CCS technology 2 for period i + model.CI_RET_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0.233) + + #Binary variable for power generation by power plant s for period i + model.A = pyo.Var(i, model.S, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of CCS technology 1 in power plant s for period i + model.B = pyo.Var(i, model.S, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of CCS technology 2 in power plant s for period i + model.C = pyo.Var(i, model.S, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of EP-NETs technology 1 for period i + model.D = pyo.Var(i, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of EP-NETs technology 2 for period i + model.E = pyo.Var(i, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of EP-NETs technology 3 for period i + model.F = pyo.Var(i, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of EC-NETs technology 1 for period i + model.G = pyo.Var(i, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of EC-NETs technology 2 for period i + model.H = pyo.Var(i, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of EC-NETs technology 3 for period i + model.I = pyo.Var(i, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of solar renewable energy for period i + model.J = pyo.Var(i, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of hydropower renewable energy for period i + model.K = pyo.Var(i, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of biomass renewable energy for period i + model.L = pyo.Var(i, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of biogas renewable energy for period i + model.M = pyo.Var(i, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of MSW renewable energy for period i + model.N = pyo.Var(i, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of alternative solid-based fuel technology 1 in power plant s for period i + model.O = pyo.Var(i, model.S, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of alternative solid-based fuel technology 2 in power plant s for period i + model.P = pyo.Var(i, model.S, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of alternative gas-based fuel technology 1 in power plant s for period i + model.Q = pyo.Var(i, model.S, domain = pyo.Binary, initialize = 0) + + #Binary variable for the deployment of alternative gas-based fuel technology 2 in power plant s for period i + model.R = pyo.Var(i, model.S, domain = pyo.Binary, initialize = 0) + + #This variable represents the deployment of CCS technology 1 in power plant s for period i + model.CCS_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable represents the deployment of CCS technology 2 in power plant s for period i + model.CCS_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the net energy available from power plant s without CCS deployment for period i + model.net_energy = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the net energy available from power plant s with the deployment of CCS technology 1 for period i + model.net_energy_CCS_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the net energy available from power plant s with the deployment of CCS technology 2 for period i + model.net_energy_CCS_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable represents the minimum deployment of EP_NETs technology 1 for period i + model.EP_NET_1 = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable represents the minimum deployment of EP_NETs technology 2 for period i + model.EP_NET_2 = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable represents the minimum deployment of EP_NETs technology 3 for period i + model.EP_NET_3 = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable represents the minimum deployment of EC_NETs technology 1 for period i + model.EC_NET_1 = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable represents the minimum deployment of EC_NETs technology 2 for period i + model.EC_NET_2 = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable represents the minimum deployment of EC_NETs technology 3 for period i + model.EC_NET_3 = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the minimum deployment of solar renewable energy for period i + model.REN_SOLAR = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the minimum deployment of hydro renewable energy for period i + model.REN_HYDRO = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the minimum deployment of biomass renewable energy for period i + model.REN_BM = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the minimum deployment of biogas renewable energy for period i + model.REN_BG = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the minimum deployment of MSW renewable energy for period i + model.REN_MSW = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the minimum deployment of alternative solid-based fuel technology 1 for coal-based plant s for period i + model.solid_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the minimum deployment of alternative solid-based fuel technology 2 for coal-based plant s for period i + model.solid_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the minimum deployment of alternative gas-based fuel technology 1 for natural gas-based plant s for period i + model.gas_1 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the minimum deployment of alternative gas-based fuel technology 2 for natural gas-based plant s for period i + model.gas_2 = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the revised total CO2 emissions for period i + model.new_emission = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 23.64428683) + + #This variable determines the total energy cost (fuel and annualized capital) of power plant s for period i + model.energy_cost = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 16.473) + + #This variable determines the fuel energy cost of power plant s for period i + model.fuel_cost = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0.018) + + #This variable determines the annualized capital energy cost of power plant s for period i + model.annualized_capital_cost = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 16.455) + + #This variable determines the total energy planning cost for period i + model.sum_cost = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 12089.43882) + + #This variable determines the net energy available from power plant s with CCS deployment for period i + model.net_energy_CCS = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable shows the emissions bought by each powerplant s from government for period i + model.CT_emissions = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable defines the perunit emissions price of a power plant s for period i + model.emissions_unit_cost = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, bounds = (0, 1000000), initialize = 0) + + #This variable shows net emissions from a plant s for period i + model.net_plant_emissions = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable shows the earnings of a power plant by selling stored emissions + model.CCS_selling_price = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable shows the availability of tradable carbon emissions between plants + model.ET_plant = pyo.Var(i, model.S, bounds = (-100000, 100000), initialize = 0) + + #This variable shows the carbon emissions trading between plants + model.ET_plant_final = pyo.Var(i, model.S, bounds = (-100000, 100000), initialize = 0) + + #This variable shows the cost paid/earned by each powerplant s in trading emissions among themselves for period i + model.CT_cost = pyo.Var(i, model.S, bounds = (-1000000, 1000000), initialize = 0) + + #This variable shows the traded carbon emissions in period i from the previous period i-1 + model.ET_plant_new = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 0) + + #This variable determines the total CO2 emission rights left to trade in next period for period i + model.CT_balance_emissions = pyo.Var(i, initialize = 0) + + #This variable determines the total CO2 emission rights left to trade for a plant s in next period i + model.Emissions_left = pyo.Var(i, model.S, bounds = (-100000, 100000), initialize = 0) + + #This variable predicts the electricity prices for each period i + model.Electricity_price = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 120) + + #This variable shows the overall cost of a plant s for period i + model.Plant_cost = pyo.Var(i, model.S, initialize = 16.473) + + #This variable shows the earnings of a power plant by selling electricty + model.Plant_earning = pyo.Var(i, model.S, domain = pyo.NonNegativeReals, initialize = 99) + + #This variable determines the total energy planning cost for period i + model.total_profit = pyo.Var(i, domain = pyo.NonNegativeReals, initialize = 7413.561183) + + + #OBJECTIVE FUNCTION + + #For the minimum budget objective function, the total cost is minimised, subject to the satisfaction of the CO2 emission limit + #For the minimum emission objective function, the total emission is minimised, subject to the available budgetary constraint + #For the maximum profit objective function, the total profit or net cost is maximized, subject to the satisfaction of the CO2 emission limit and allocated budget + if flag == 'min_budget': + model.obj = pyo.Objective(expr = sum(model.sum_cost[i] for i in periods), sense = pyo.minimize) + elif flag == 'min_emission': + model.obj = pyo.Objective(expr = sum(model.new_emission[i] for i in periods), sense = pyo.minimize) + else: + model.obj = pyo.Objective(expr = sum(model.total_profit[i] for i in periods), sense = pyo.maximize) + + + #CONSTRAINTS + + + #Prior to any energy planning, the total power generation from all power plants should satisfy the regional energy demand for period i + def demand(model, i, s): + return model.EP['Demand'][i] == sum(model.energy[i,s] for s in model.S) + + model.Cons_1 = pyo.Constraint(i, model.S, rule = demand) + + + #The deployment of energy source in power plant s should at least satisfy the lower bound for period i + def lower_bound_energy(model, i, s): + return model.energy[i,s] >= model.plant[s]['LB'] * model.A[i,s] + + model.Cons_2 = pyo.Constraint(i, model.S, rule = lower_bound_energy) + + + #The deployment of energy source in power plant s should at most satisfy the upper bound for period i + def upper_bound_energy(model, i, s): + return model.energy[i,s] <= model.plant[s]['UB'] * model.A[i,s] + + model.Cons_3 = pyo.Constraint(i, model.S, rule = upper_bound_energy) + + + #There should not be power generation from power plant s before its commissioning period + def plant_commission(model, i, s): + if i < model.plant[s]['ON']: + return model.energy[i,s] == 0 + else: + return model.energy[i,s] >= 0 + + model.Cons_4 = pyo.Constraint(i, model.S, rule = plant_commission) + + + #There should be power generation from power plant s after its decommissioning period + def plant_decommission(model, i, s): + if i >= model.plant[s]['OFF']: + return model.energy[i,s] == 0 + else: + return model.energy[i,s] >= 0 + + model.Cons_5 = pyo.Constraint(i, model.S, rule = plant_decommission) + + + #If power plant s is decomissioned in a period, it should remain decommissioned at later periods + #However, power plant s should be available for power generation till the period before its decommissioning period + def energy_constraint(model, i, s): + if i <= model.plant[s]['OFF'] - 2: + return model.energy[i+1,s] >= model.energy[i,s] + else: + return pyo.Constraint.Skip + + model.Cons_6 = pyo.Constraint(i, model.S, rule = energy_constraint) + + + #Calculation of carbon intensity of energy sources with CCS technology 1 in power plant s for period i + def CCS_CI_1(model, i, s): + return model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i]) == model.CI_RET_1[i,s] + + model.Cons_7 = pyo.Constraint(i, model.S, rule = CCS_CI_1) + + + #Calculation of carbon intensity of energy sources with CCS technology 2 in power plant s for period i + def CCS_CI_2(model, i, s): + return model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i]) == model.CI_RET_2[i,s] + + model.Cons_8 = pyo.Constraint(i, model.S, rule = CCS_CI_2) + + + #If selected, the deployment of CCS technology 1 in power plant s is limited by the upper bound of the energy output for period i + def CCS_limit_1(model, i, s): + return model.CCS_1[i,s] <= model.plant[s]['UB'] * model.B[i,s] + + model.Cons_9 = pyo.Constraint(i, model.S, rule = CCS_limit_1) + + + #If selected, the deployment of CCS technology 2 in power plant s is limited by the upper bound of the energy output for period i + def CCS_limit_2(model, i, s): + return model.CCS_2[i,s] <= model.plant[s]['UB'] * model.C[i,s] + + model.Cons_10 = pyo.Constraint(i, model.S, rule = CCS_limit_2) + + + #The total CCS deployment in power plant s should be equal to summation of deployment of individual types of CCS technology for period i + def CCS_total(model, i, s): + return model.CCS_1[i,s] + model.CCS_2[i,s] <= model.energy[i,s] + + model.Cons_11 = pyo.Constraint(i, model.S, rule = CCS_total) + + + #The deployment of CCS technology 1 at later periods should at least match the deployment in the previous period + def CCS_1_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.CCS_1[i+1,s] >= model.CCS_1[i,s] + + model.Cons_12 = pyo.Constraint(i, model.S, rule = CCS_1_constraint) + + + #The deployment of CCS technology 2 at later periods should at least match the deployment in the previous period + def CCS_2_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.CCS_2[i+1,s] >= model.CCS_2[i,s] + + model.Cons_13 = pyo.Constraint(i, model.S, rule = CCS_2_constraint) + + + #Determine the net energy available from power plant s with CCS technology 1 for period i + def CCS_1_net_energy(model, i, s): + return model.CCS_1[i,s] * (1 - model.CCS_data['X_1'][i]) == model.net_energy_CCS_1[i,s] + + model.Cons_14 = pyo.Constraint(i, model.S, rule = CCS_1_net_energy) + + + #Determine the net energy available from power plant s with CCS technology 2 for period i + def CCS_2_net_energy(model, i, s): + return model.CCS_2[i,s] * (1 - model.CCS_data['X_2'][i]) == model.net_energy_CCS_2[i,s] + + model.Cons_15 = pyo.Constraint(i, model.S, rule = CCS_2_net_energy) + + + #The deployment of alternative solid fuel technology 1 at later periods should at least match the deployment in the previous period + def alt_solid_1_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.solid_1[i+1,s] >= model.solid_1[i,s] + + model.Cons_16 = pyo.Constraint(i, model.S, rule = alt_solid_1_constraint) + + + #The deployment of alternative solid fuel technology 2 at later periods should at least match the deployment in the previous period + def alt_solid_2_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.solid_2[i+1,s] >= model.solid_2[i,s] + + model.Cons_17 = pyo.Constraint(i, model.S, rule = alt_solid_2_constraint) + + + #The deployment of alternative gas fuel technology 1 at later periods should at least match the deployment in the previous period + def alt_gas_1_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.gas_1[i+1,s] >= model.gas_1[i,s] + + model.Cons_18 = pyo.Constraint(i, model.S, rule = alt_gas_1_constraint) + + + #The deployment of alternative gas fuel technology 2 at later periods should at least match the deployment in the previous period + def alt_gas_2_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.gas_2[i+1,s] >= model.gas_2[i,s] + + model.Cons_19 = pyo.Constraint(i, model.S, rule = alt_gas_2_constraint) + + + #The total energy contribution must equal the initially determined energy contribution of power plant s for period i + def fuel_substitution(model, i, s): + if 'REN' in model.plant[s].values(): + return model.net_energy[i,s] == model.energy[i,s] + elif 'NUCLEAR' in model.plant[s].values(): + return model.net_energy[i,s] == model.energy[i,s] + elif 'NG' in model.plant[s].values(): + return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] + model.gas_1[i,s] + model.gas_2[i,s] == model.energy[i,s] + elif 'COAL' in model.plant[s].values(): + return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] == model.energy[i,s] + else: + return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] == model.energy[i,s] + + model.Cons_20 = pyo.Constraint(i, model.S, rule = fuel_substitution) + + + #If power plant s is fuelled by renewable energy sources, CCS technology 1 would not be deployed for period i + def no_CCS_1(model, i, s): + if 'REN' in model.plant[s].values(): + return model.CCS_1[i,s] == 0 + elif 'NUCLEAR' in model.plant[s].values(): + return model.CCS_1[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_21 = pyo.Constraint(i, model.S, rule = no_CCS_1) + + + #If power plant s is fuelled by renewable energy sources, CCS technology 1 would not be deployed for period i + def no_CCS_2(model, i, s): + if 'REN' in model.plant[s].values(): + return model.CCS_2[i,s] == 0 + elif 'NUCLEAR' in model.plant[s].values(): + return model.CCS_2[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_22 = pyo.Constraint(i, model.S, rule = no_CCS_2) + + + #If power plant s is fuelled by renewable energy sources, alternative fuels would not be deployed for period i + def no_AF(model, i, s): + if 'REN' in model.plant[s].values(): + return model.gas_1[i,s] + model.gas_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] == 0 + elif 'NUCLEAR' in model.plant[s].values(): + return model.gas_1[i,s] + model.gas_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_23 = pyo.Constraint(i, model.S, rule = no_AF) + + + #If power plant s is fuelled by renewable energy sources, alternative fuels would not be deployed for period i + def no_AF_selection(model, i, s): + if 'REN' in model.plant[s].values(): + return model.O[i,s] + model.P[i,s] + model.Q[i,s] + model.R[i,s] == 0 + elif 'NUCLEAR' in model.plant[s].values(): + return model.O[i,s] + model.P[i,s] + model.Q[i,s] + model.R[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_24 = pyo.Constraint(i, model.S, rule = no_AF_selection) + + + #Technology implementation time for CCS technology 1 + def deployment_CCS_1(model, i, s): + if model.TIME['CCS_1'][i] == 'NO': + return model.B[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_25 = pyo.Constraint(i, model.S, rule = deployment_CCS_1) + + + #Technology implementation time for CCS technology 2 + def deployment_CCS_2(model, i, s): + if model.TIME['CCS_2'][i] == 'NO': + return model.C[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_26 = pyo.Constraint(i, model.S, rule = deployment_CCS_2) + + + #Technology implementation time for EP-NETs technology 1 + def deployment_EP_NETs_1(model, i): + if model.TIME['EP_NETs_1'][i] == 'NO': + return model.D[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_27 = pyo.Constraint(i, rule = deployment_EP_NETs_1) + + + #Technology implementation time for EP-NETs technology 2 + def deployment_EP_NETs_2(model, i): + if model.TIME['EP_NETs_2'][i] == 'NO': + return model.E[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_28 = pyo.Constraint(i, rule = deployment_EP_NETs_2) + + + #Technology implementation time for EP-NETs technology 3 + def deployment_EP_NETs_3(model, i): + if model.TIME['EP_NETs_3'][i] == 'NO': + return model.F[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_29 = pyo.Constraint(i, rule = deployment_EP_NETs_3) + + + #Technology implementation time for EC-NETs technology 1 + def deployment_EC_NETs_1(model, i): + if model.TIME['EC_NETs_1'][i] == 'NO': + return model.G[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_30 = pyo.Constraint(i, rule = deployment_EC_NETs_1) + + + #Technology implementation time for EC-NETs technology 2 + def deployment_EC_NETs_2(model, i): + if model.TIME['EC_NETs_2'][i] == 'NO': + return model.H[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_31 = pyo.Constraint(i, rule = deployment_EC_NETs_2) + + + #Technology implementation time for EC-NETs technology 3 + def deployment_EC_NETs_3(model, i): + if model.TIME['EC_NETs_3'][i] == 'NO': + return model.I[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_32 = pyo.Constraint(i, rule = deployment_EC_NETs_3) + + + #Technology implementation time for solar compensatory energy + def deployment_solar(model, i): + if model.TIME['SOLAR'][i] == 'NO': + return model.J[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_33 = pyo.Constraint(i, rule = deployment_solar) + + + #Technology implementation time for hydropower compensatory energy + def deployment_hydro(model, i): + if model.TIME['HYDRO'][i] == 'NO': + return model.K[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_34 = pyo.Constraint(i, rule = deployment_hydro) + + + #Technology implementation time for biomass compensatory energy + def deployment_biomass(model, i): + if model.TIME['BIOMASS'][i] == 'NO': + return model.L[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_35 = pyo.Constraint(i, rule = deployment_biomass) + + + #Technology implementation time for biogas compensatory energy + def deployment_biogas(model, i): + if model.TIME['BIOGAS'][i] == 'NO': + return model.M[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_36 = pyo.Constraint(i, rule = deployment_biogas) + + + #Technology implementation time for MSW compensatory energy + def deployment_MSW(model, i): + if model.TIME['MSW'][i] == 'NO': + return model.N[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_37 = pyo.Constraint(i, rule = deployment_MSW) + + + #Technology implementation time for alternative solid fuel type 1 + def deployment_alt_solid_1(model, i, s): + if model.TIME['SOLID_1'][i] == 'NO': + return model.O[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_38 = pyo.Constraint(i, model.S, rule = deployment_alt_solid_1) + + + #Technology implementation time for alternative solid fuel type 2 + def deployment_alt_solid_2(model, i, s): + if model.TIME['SOLID_2'][i] == 'NO': + return model.P[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_39 = pyo.Constraint(i, model.S, rule = deployment_alt_solid_2) + + + #Technology implementation time for alternative gas fuel type 1 + def deployment_alt_gas_1(model, i, s): + if model.TIME['GAS_1'][i] == 'NO': + return model.Q[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_40 = pyo.Constraint(i, model.S, rule = deployment_alt_gas_1) + + + #Technology implementation time for alternative gas fuel type 2 + def deployment_alt_gas_2(model, i, s): + if model.TIME['GAS_2'][i] == 'NO': + return model.R[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_41 = pyo.Constraint(i, model.S, rule = deployment_alt_gas_2) + + + #Big M formulation for EP-NETs technology 1 deployment for period i + def big_M_EP_NETs_1(model, i): + return model.EP_NET_1[i] <= model.D[i] * 1000 + + model.Cons_42 = pyo.Constraint(i, rule = big_M_EP_NETs_1) + + + #Big M formulation for EP-NETs technology 2 deployment for period i + def big_M_EP_NETs_2(model, i): + return model.EP_NET_2[i] <= model.E[i] * 1000 + + model.Cons_43 = pyo.Constraint(i, rule = big_M_EP_NETs_2) + + + #Big M formulation for EP-NETs technology 3 deployment for period i + def big_M_EP_NETs_3(model, i): + return model.EP_NET_3[i] <= model.F[i] * 1000 + + model.Cons_44 = pyo.Constraint(i, rule = big_M_EP_NETs_3) + + + #Big M formulation for EC-NETs technology 1 deployment for period i + def big_M_EC_NETs_1(model, i): + return model.EC_NET_1[i] <= model.G[i] * 1000 + + model.Cons_45 = pyo.Constraint(i, rule = big_M_EC_NETs_1) + + + #Big M formulation for EC-NETs technology 2 deployment for period i + def big_M_EC_NETs_2(model, i): + return model.EC_NET_2[i] <= model.H[i] * 1000 + + model.Cons_46 = pyo.Constraint(i, rule = big_M_EC_NETs_2) + + + #Big M formulation for EC-NETs technology 3 deployment for period i + def big_M_EC_NETs_3(model, i): + return model.EC_NET_3[i] <= model.I[i] * 1000 + + model.Cons_47 = pyo.Constraint(i, rule = big_M_EC_NETs_3) + + + #Big M formulation for deployment of solar renewable energy for period i + def big_M_solar(model, i): + return model.REN_SOLAR[i] <= model.J[i] * 1000 + + model.Cons_48 = pyo.Constraint(i, rule = big_M_solar) + + + #Big M formulation for deployment of hydro renewable energy for period i + def big_M_hydro(model, i): + return model.REN_HYDRO[i] <= model.K[i] * 1000 + + model.Cons_49 = pyo.Constraint(i, rule = big_M_hydro) + + + #Big M formulation for deployment of BM renewable energy for period i + def big_M_biomass(model, i): + return model.REN_BM[i] <= model.L[i] * 1000 + + model.Cons_50 = pyo.Constraint(i, rule = big_M_biomass) + + + #Big M formulation for deployment of BG renewable energy for period i + def big_M_biogas(model, i): + return model.REN_BG[i] <= model.M[i] * 1000 + + model.Cons_51 = pyo.Constraint(i, rule = big_M_biogas) + + + #Big M formulation for deployment of MSW renewable energy for period i + def big_M_MSW(model, i): + return model.REN_MSW[i] <= model.N[i] * 1000 + + model.Cons_52 = pyo.Constraint(i, rule = big_M_MSW) + + + #Big M formulation for deployment of alternative solid fuel type 1 for period i + def big_M_alt_solid_1(model, i, s): + return model.solid_1[i,s] <= model.O[i,s] * model.plant[s]['UB'] + + model.Cons_53 = pyo.Constraint(i, model.S, rule = big_M_alt_solid_1) + + + #Big M formulation for deployment of alternative solid fuel type 2 for period i + def big_M_alt_solid_2(model, i, s): + return model.solid_2[i,s] <= model.P[i,s] * model.plant[s]['UB'] + + model.Cons_54 = pyo.Constraint(i, model.S, rule = big_M_alt_solid_2) + + + #Big M formulation for deployment of alternative gas fuel type 1 for period i + def big_M_alt_gas_1(model, i, s): + return model.gas_1[i,s] <= model.Q[i,s] * model.plant[s]['UB'] + + model.Cons_55 = pyo.Constraint(i, model.S, rule = big_M_alt_gas_1) + + + #Big M formulation for deployment of alternative gas fuel type 2 for period i + def big_M_alt_gas_2(model, i, s): + return model.gas_2[i,s] <= model.R[i,s] * model.plant[s]['UB'] + + model.Cons_56 = pyo.Constraint(i, model.S, rule = big_M_alt_gas_2) + + + #The deployment of solar renewable energy at later periods should at least match the deployment in the previous period + def solar_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.REN_SOLAR[i+1] >= model.REN_SOLAR[i] + + model.Cons_57 = pyo.Constraint(i, model.S, rule = solar_time_constraint) + + + #The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period + def hydro_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.REN_HYDRO[i+1] >= model.REN_HYDRO[i] + + model.Cons_58 = pyo.Constraint(i, model.S, rule = hydro_time_constraint) + + + #The deployment of solar renewable energy at later periods should at least match the deployment in the previous period + def biomass_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.REN_BM[i+1] >= model.REN_BM[i] + + model.Cons_59 = pyo.Constraint(i, model.S, rule = biomass_time_constraint) + + + #The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period + def biogas_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.REN_BG[i+1] >= model.REN_BG[i] + + model.Cons_60 = pyo.Constraint(i, model.S, rule = biogas_time_constraint) + + + #The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period + def MSW_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.REN_MSW[i+1] >= model.REN_MSW[i] + + model.Cons_61 = pyo.Constraint(i, model.S, rule = MSW_time_constraint) + + + #The deployment of EP-NETs technology 1 at later periods should at least match the deployment in the previous period + def EP_NETs_1_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.EP_NET_1[i+1] >= model.EP_NET_1[i] + + model.Cons_62 = pyo.Constraint(i, model.S, rule = EP_NETs_1_time_constraint) + + + #The deployment of EP-NETs technology 2 at later periods should at least match the deployment in the previous period + def EP_NETs_2_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.EP_NET_2[i+1] >= model.EP_NET_2[i] + + model.Cons_63 = pyo.Constraint(i, model.S, rule = EP_NETs_2_time_constraint) + + + #The deployment of EP-NETs technology 3 at later periods should at least match the deployment in the previous period + def EP_NETs_3_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.EP_NET_3[i+1] >= model.EP_NET_3[i] + + model.Cons_64 = pyo.Constraint(i, model.S, rule = EP_NETs_3_time_constraint) + + + #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period + def EC_NETs_1_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.EC_NET_1[i+1] >= model.EC_NET_1[i] + + model.Cons_65 = pyo.Constraint(i, model.S, rule = EC_NETs_1_time_constraint) + + + #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period + def EC_NETs_2_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.EC_NET_2[i+1] >= model.EC_NET_2[i] + + model.Cons_66 = pyo.Constraint(i, model.S, rule = EC_NETs_2_time_constraint) + + + #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period + def EC_NETs_3_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.EC_NET_3[i+1] >= model.EC_NET_3[i] + + model.Cons_67 = pyo.Constraint(i, model.S, rule = EC_NETs_3_time_constraint) + + + #Total energy contribution from all energy sources to satisfy the total demand for period i + def total_energy(model, i, s): + return sum((model.net_energy[i,s] + model.net_energy_CCS_1[i,s] + model.net_energy_CCS_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] + model.gas_1[i,s] + model.gas_2[i,s]) for s in model.S) + model.REN_SOLAR[i] + model.REN_HYDRO[i] + model.REN_BM[i] + model.REN_BG[i] + model.REN_MSW[i] + model.EP_NET_1[i] + model.EP_NET_2[i] + model.EP_NET_3[i] == model.EP['Demand'][i] + model.EC_NET_1[i] + model.EC_NET_2[i] + model.EC_NET_3[i] + + model.Cons_68 = pyo.Constraint(i, model.S, rule = total_energy) + + #Determine the net energy available from power plant s with the deployment of CCS technologies 1 & 2 for period i + def CCS_net_energy(model, i, s): + return model.net_energy_CCS_1[i,s] + model.net_energy_CCS_2[i,s] == model.net_energy_CCS[i,s] + + model.Cons_69 = pyo.Constraint(i, model.S, rule = CCS_net_energy) + + + #This constraint defines the mminimum limit for per unit emissions price to be paid by a power plant s for period i + def Emissions_unit_cost_LB(model, i, s): + if CT == 'ON': + return (model.emissions_unit_cost[i,s]) >= (model.EP['Emissions Price LB'][i]) * model.A[i,s] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_70 = pyo.Constraint(i, model.S, rule = Emissions_unit_cost_LB) + + + #Constraints below are related to carbon trading model + #This constraint forbids the non-generation plants to participate in CT + def Emissions_unit_cost_value(model, i, s): + if CT == 'ON': + return model.emissions_unit_cost[i,s] == model.A[i,s] * (model.emissions_unit_cost[i,s]) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_71 = pyo.Constraint(i, model.S, rule = Emissions_unit_cost_value) + + + #This constraint puts upper limit to the emissions bought from the government by each plant s through carbon trading for period i + def CT_emissions_UL (model, i, s): + if CT == 'ON': + return model.CT_emissions[i,s] <= model.plant[s]['UB'] * model.plant[s]['CI'] * model.A[i,s] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_72 = pyo.Constraint(i, model.S, rule = CT_emissions_UL) + + + #This constraint puts lower limit to the emissions bought from the government by each plant s through carbon trading for period i + def CT_emissions_LL (model, i, s): + if CT == 'ON': + return model.CT_emissions[i,s] >= model.plant[s]['LB'] * model.plant[s]['CI'] * model.A[i,s] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_73 = pyo.Constraint(i, model.S, rule = CT_emissions_LL) + + + #This constraint calculates net emissions from a plant s for period i + def CO2_load_plant(model, i, s): + return ((model.net_energy[i,s] * model.plant[s]['CI']) + (model.net_energy_CCS_1[i,s] * model.plant[s]['CI'] * ((1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i]))) + (model.net_energy_CCS_2[i,s] * model.plant[s]['CI'] * ((1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i]))) + (model.solid_1[i,s] * model.SLD_CI['SOLID_1'][i]) + (model.solid_2[i,s] * model.SLD_CI['SOLID_2'][i]) + (model.gas_1[i,s] * model.GAS_CI['GAS_1'][i]) + (model.gas_2[i,s] * model.GAS_CI['GAS_2'][i])) == model.net_plant_emissions[i,s] + + model.Cons_74 = pyo.Constraint(i, model.S, rule = CO2_load_plant) + + + #This constraint calculates the emissions avaiable for trading between plants by each plant s for period i + def CT_emissions_tradable (model, i, s): + if CT == 'ON': + return ((model.ET_plant_new [i,s] + model.CT_emissions[i,s]) - (model.net_plant_emissions[i,s])) == model.ET_plant [i,s] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_75 = pyo.Constraint(i, model.S, rule = CT_emissions_tradable) + + + #This constraint balances the emissions for plant s in period i + def CT_plant_emissions_balance (model, i, s): + if CT == 'ON': + return model.ET_plant [i,s] == model.ET_plant_final [i,s] + model.Emissions_left [i,s] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_76 = pyo.Constraint(i, model.S, rule = CT_plant_emissions_balance) + + + #This constraint calculates balance emissions for all periods (except first one) + def CT_rem_balance_emissions (model, i, s): + if CT == 'ON': + return model.CT_balance_emissions [i] == sum(model.Emissions_left [i,s] for s in model.S) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_77 = pyo.Constraint(i, model.S, rule = CT_rem_balance_emissions) + + + #This constraint calculates balance emissions for all periods (except first one) + def CT_trade_within_plants (model, i, s): + if CT == 'ON': + return sum(model.ET_plant_final[i,s] for s in model.S) == 0 + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_78 = pyo.Constraint(i, model.S, rule = CT_trade_within_plants) + + + #This constraint quantifies the emissions avaible for a plant to be traded within a period + def CT_plant_emissions_value (model, i, s): + if CT == 'ON': + return model.ET_plant_final [i,s] == model.ET_plant_final[i,s] * model.A[i,s] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_79 = pyo.Constraint(i, model.S, rule = CT_plant_emissions_value) + + + #This constraint enables puts an upper limit on emissions trading between plants + def CT_plant_UL_final (model, i, s): + if CT == 'ON': + return model.ET_plant_final [i,s] <= (CT_Percentage_Emissions) * (model.energy[i,s] * (model.plant[s]['CI'])) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_80 = pyo.Constraint(i, model.S, rule = CT_plant_UL_final) + + + #This constraint enables puts a lower limit on emissions trading between plants + def CT_plant_LL_final (model, i, s): + if CT == 'ON': + return model.ET_plant_final [i,s] >= (-1) * (CT_Percentage_Emissions) * (model.energy[i,s] * (model.plant[s]['CI'])) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_81 = pyo.Constraint(i, model.S, rule = CT_plant_LL_final) + + + #This constraint puts limit to balance emissions traded in next period + def CT_balance_emissions_trading (model, i, s): + if CT == 'ON': + if i == 1: + return sum(model.ET_plant_new [i,s] for s in model.S) <= EB + else: + return sum(model.ET_plant_new [i,s] for s in model.S) <= model.CT_balance_emissions[i-1] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_82 = pyo.Constraint(i, model.S, rule = CT_balance_emissions_trading) + + + #This constraint enables puts an upper limit on emissions between periods + def CT_plant_new_UL (model, i, s): + if CT == 'ON': + return model.ET_plant_new [i,s] <= (model.plant[s]['UB'] * model.plant[s]['CI'] * model.A[i,s]) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_83 = pyo.Constraint(i, model.S, rule = CT_plant_new_UL) + + + #This constraint puts a budget cap on carbon trading + def CT_budget_cap (model, i, s): + if CT == 'ON': + return ((sum((model.CT_emissions[i,s] * model.emissions_unit_cost[i,s]) for s in model.S)) + (model.EP['CT Additional Budget'][i])) >= ((-1) * (sum((model.Emissions_left [i,s] * model.emissions_unit_cost[i,s]) for s in model.S))) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_84 = pyo.Constraint(i, model.S, rule = CT_budget_cap) + + + #This constraint enables trading within different periods + def CT_periods (model, i, s): + if CT == 'ON': + return sum(model.ET_plant_new[i,s] for s in model.S) + sum(model.ET_plant_final[i,s] for s in model.S) + sum(model.CT_emissions[i,s] for s in model.S) == (sum((model.energy[i,s] * (model.plant[s]['CI'])) for s in model.S)) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_85 = pyo.Constraint(i, model.S, rule = CT_periods) + + + #This constraint calculates the cost paid/earned by each plant s to buy or sell carbon emissions among each other for period i + def CT_cost (model, i, s): + if CT == 'ON': + return model.CT_cost[i,s] == model.emissions_unit_cost[i,s] * (model.CT_emissions[i,s] + model.ET_plant_new [i,s] + model.ET_plant_final [i,s] + model.Emissions_left [i,s]) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_86 = pyo.Constraint(i, model.S, rule = CT_cost) + + + #This constraint defines the earning of a power plant s by selling stored emissions as a result of implementing CCS for period i + def CO2_selling(model, i, s): + return ((((model.net_energy_CCS[i,s])*(model.plant[s]['CI'])) - ((model.net_energy_CCS_1[i,s]) * (model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i]))) - ((model.net_energy_CCS_2[i,s]) * (model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i])))) * (model.EP['Carbon Emissions Price'][i])) == (model.CCS_selling_price[i,s]) + + model.Cons_87 = pyo.Constraint(i, model.S, rule = CO2_selling) + + + #This constraint limits the stored emissions as a result of CCS for period i + def Stored_emissions_limit(model, i, s): + return model.EP['Carbon Emissions Requirement'][i] >= sum((((model.net_energy_CCS_1[i,s]) * (model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i]))) + ((model.net_energy_CCS_2[i,s])*(model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i])))) for s in model.S) + + model.Cons_88 = pyo.Constraint(i, model.S, rule = Stored_emissions_limit) + + + #Determining the total earning of a power plant s for period i + def Plant_earning(model, i, s): + if 'SOLAR' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Solar)'][i])) + elif 'WIND' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Wind)'][i])) + elif 'HYDRO' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Hydro)'][i])) + elif 'BIOGAS' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Biogas)'][i])) + elif 'BIOMASS' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Biomass)'][i])) + elif 'MSW' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (MSW)'][i])) + elif 'NUCLEAR' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Nuclear)'][i])) + elif 'NG' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (NG)'][i])) + elif 'OIL' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Oil)'][i])) + else: + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Coal)'][i])) + + model.Cons_89 = pyo.Constraint(i, model.S, rule = Plant_earning) + + + #This constraint puts constraint to electricity prices + def Electricity_Prices(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.Electricity_price[i+1,s] >= (model.Electricity_price[i,s]) * ((1)+(Elec_Price_Inflation)) + + model.Cons_90 = pyo.Constraint(i, model.S, rule = Electricity_Prices) + + + #This constraint defines the initial electricity price for first period and puts upper bound to electricity prices + def Electricity_Price_P1(model, i, s): + if i == 1: + return model.Electricity_price[i,s] == (Initial_Elec_Price) + else: + return model.Electricity_price[i,s] <= (Max_Elec_Price) + + model.Cons_91 = pyo.Constraint(i, model.S, rule = Electricity_Price_P1) + + + #The total CO2 load contribution from all energy sources must satisfy most the CO2 emission limit in period i + def total_CO2_load(model, i, s): + return (sum((model.net_energy[i,s] * model.plant[s]['CI']) + (model.net_energy_CCS_1[i,s] * model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i])) + (model.net_energy_CCS_2[i,s] * model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i])) + + (model.solid_1[i,s] * model.SLD_CI['SOLID_1'][i]) + (model.solid_2[i,s] * model.SLD_CI['SOLID_2'][i]) + + (model.gas_1[i,s] * model.GAS_CI['GAS_1'][i]) + (model.gas_2[i,s] * model.GAS_CI['GAS_2'][i]) for s in model.S) + + (model.EC_NET_1[i] * model.NET_CI['EC_NETs_1'][i]) + + (model.EC_NET_2[i] * model.NET_CI['EC_NETs_2'][i]) + + (model.EC_NET_3[i] * model.NET_CI['EC_NETs_3'][i]) + + (model.EP_NET_1[i] * model.NET_CI['EP_NETs_1'][i]) + + (model.EP_NET_2[i] * model.NET_CI['EP_NETs_2'][i]) + + (model.EP_NET_3[i] * model.NET_CI['EP_NETs_3'][i]) + + (model.REN_SOLAR[i] * model.REN_CI['SOLAR'][i]) + + (model.REN_HYDRO[i] * model.REN_CI['HYDRO'][i]) + + (model.REN_BM[i] * model.REN_CI['BIOMASS'][i]) + + (model.REN_BG[i] * model.REN_CI['BIOGAS'][i]) + + (model.REN_MSW[i] * model.REN_CI['MSW'][i]) == model.new_emission[i]) + + model.Cons_92 = pyo.Constraint(i, model.S, rule = total_CO2_load) + + + #Determining the cumulative total fuel and annualised capital cost for a power plant s for period i + def energy_cost(model, i, s): + if 'SOLAR' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['SOLAR'][i]) + elif 'WIND' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['WIND'][i]) + (AFF * model.CPX_1['WIND'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['WIND'][i]) + elif 'HYDRO' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['HYDRO'][i]) + elif 'BIOGAS' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOGAS'][i]) + elif 'BIOMASS' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOMASS'][i]) + elif 'MSW' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['MSW'][i]) + elif 'NUCLEAR' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['NUCLEAR'][i]) + (AFF * model.CPX_1['NUCLEAR'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['NUCLEAR'][i]) + elif 'NG' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['NG'][i]) + (AFF * model.CPX_1['NG'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['NG'][i]) + elif 'OIL' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['OIL'][i]) + (AFF * model.CPX_1['OIL'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['OIL'][i]) + else: + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['COAL'][i]) + (AFF * model.CPX_1['COAL'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['COAL'][i]) + + model.Cons_93 = pyo.Constraint(i, model.S, rule = energy_cost) + + + #The summation of cost for each power plant s should equal to the total cost of each period i + def Plant_net_cost(model, i, s): + if CT == 'OFF': + return ((model.Plant_earning[i,s]) - ((model.net_energy_CCS_1[i,s] * model.CCS_data['Cost_CCS_1'][i]) + (model.net_energy_CCS_2[i,s] * model.CCS_data['Cost_CCS_2'][i]) + + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]) + + (model.solid_1[i,s] * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]) + (model.solid_2[i,s] * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]) + + (model.gas_1[i,s] * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]) + (model.gas_2[i,s] * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]) + + (model.energy_cost[i,s]) - (model.CCS_selling_price[i,s]))) == (model.Plant_cost[i,s]) + + elif CT == 'ON': + return (model.Plant_earning[i,s]) - ((model.net_energy_CCS_1[i,s] * model.CCS_data['Cost_CCS_1'][i]) + (model.net_energy_CCS_2[i,s] * model.CCS_data['Cost_CCS_2'][i]) + + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]) + + (model.solid_1[i,s] * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]) + (model.solid_2[i,s] * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]) + + (model.gas_1[i,s] * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]) + (model.gas_2[i,s] * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]) + + (model.energy_cost[i,s]) + (model.CT_cost[i,s]) - (model.CCS_selling_price[i,s])) == (model.Plant_cost[i,s]) + + model.Cons_94 = pyo.Constraint(i, model.S, rule = Plant_net_cost) + + + #Determining the total fuel cost for a power plant s for period i + def fuel_cost(model, i, s): + if 'SOLAR' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['SOLAR'][i]) + elif 'WIND' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['WIND'][i]) + elif 'HYDRO' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['HYDRO'][i]) + elif 'BIOGAS' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOGAS'][i]) + elif 'BIOMASS' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOMASS'][i]) + elif 'MSW' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['MSW'][i]) + elif 'NUCLEAR' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['NUCLEAR'][i]) + elif 'NG' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['NG'][i]) + elif 'OIL' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['OIL'][i]) + else: + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['COAL'][i]) + + model.Cons_95 = pyo.Constraint(i, model.S, rule = fuel_cost) + + + #Determining the total annualised capital cost for a power plant s for period i + def annualized_capital_cost(model, i, s): + if 'SOLAR' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['SOLAR'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['SOLAR'][i]) + elif 'WIND' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['WIND'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['WIND'][i]) + elif 'HYDRO' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['HYDRO'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['HYDRO'][i]) + elif 'BIOGAS' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['BIOGAS'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOGAS'][i]) + elif 'BIOMASS' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['BIOMASS'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOMASS'][i]) + elif 'MSW' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['MSW'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['MSW'][i]) + elif 'NUCLEAR' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['NUCLEAR'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['NUCLEAR'][i]) + elif 'NG' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['NG'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['NG'][i]) + elif 'OIL' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['OIL'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['OIL'][i]) + else: + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['COAL'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['COAL'][i]) + + model.Cons_96 = pyo.Constraint(i, model.S, rule = annualized_capital_cost) + + + #The summation of cost for each power plant s should equal to the total cost of each period i + def sum_cost(model, i, s): + if CT == 'OFF': + return (sum((model.net_energy_CCS_1[i,s] * model.CCS_data['Cost_CCS_1'][i]) + (model.net_energy_CCS_2[i,s] * model.CCS_data['Cost_CCS_2'][i]) + + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]) + + (model.solid_1[i,s] * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]) + (model.solid_2[i,s] * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]) + + (model.gas_1[i,s] * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]) + (model.gas_2[i,s] * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]) for s in model.S) + + (model.EC_NET_1[i] * model.NET_COST['EC_NETs_1'][i]) + (AFF * model.CPX_1['EC_NETs_1'][i] * model.G[i]) + (AFF * model.EC_NET_1[i] * model.CPX_2['EC_NETs_1'][i]) + + (model.EC_NET_2[i] * model.NET_COST['EC_NETs_2'][i]) + (AFF * model.CPX_1['EC_NETs_2'][i] * model.H[i]) + (AFF * model.EC_NET_2[i] * model.CPX_2['EC_NETs_2'][i]) + + (model.EC_NET_3[i] * model.NET_COST['EC_NETs_3'][i]) + (AFF * model.CPX_1['EC_NETs_3'][i] * model.I[i]) + (AFF * model.EC_NET_3[i] * model.CPX_2['EC_NETs_3'][i]) + + (model.EP_NET_1[i] * model.NET_COST['EP_NETs_1'][i]) + (AFF * model.CPX_1['EP_NETs_1'][i] * model.D[i]) + (AFF * model.EP_NET_1[i] * model.CPX_2['EP_NETs_1'][i]) + + (model.EP_NET_2[i] * model.NET_COST['EP_NETs_2'][i]) + (AFF * model.CPX_1['EP_NETs_2'][i] * model.E[i]) + (AFF * model.EP_NET_2[i] * model.CPX_2['EP_NETs_2'][i]) + + (model.EP_NET_3[i] * model.NET_COST['EP_NETs_3'][i]) + (AFF * model.CPX_1['EP_NETs_3'][i] * model.F[i]) + (AFF * model.EP_NET_3[i] * model.CPX_2['EP_NETs_3'][i]) + + (model.REN_SOLAR[i] * model.REN_COST['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.J[i]) + (AFF * model.REN_SOLAR[i] * model.CPX_2['SOLAR'][i]) + + (model.REN_HYDRO[i] * model.REN_COST['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.K[i]) + (AFF * model.REN_HYDRO[i] * model.CPX_2['HYDRO'][i]) + + (model.REN_BM[i] * model.REN_COST['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.L[i]) + (AFF * model.REN_BM[i] * model.CPX_2['BIOMASS'][i]) + + (model.REN_BG[i] * model.REN_COST['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.M[i]) + (AFF * model.REN_BG[i] * model.CPX_2['BIOGAS'][i]) + + (model.REN_MSW[i] * model.REN_COST['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.N[i]) + (AFF * model.REN_MSW[i] * model.CPX_2['MSW'][i]) + + sum(model.energy_cost[i,s] for s in model.S) - sum(model.CCS_selling_price[i,s] for s in model.S) == model.sum_cost[i]) + + elif CT == 'ON': + return (sum((model.net_energy_CCS_1[i,s] * model.CCS_data['Cost_CCS_1'][i]) + (model.net_energy_CCS_2[i,s] * model.CCS_data['Cost_CCS_2'][i]) + + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]) + + (model.solid_1[i,s] * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]) + (model.solid_2[i,s] * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]) + + (model.gas_1[i,s] * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]) + (model.gas_2[i,s] * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]) for s in model.S) + + (model.EC_NET_1[i] * model.NET_COST['EC_NETs_1'][i]) + (AFF * model.CPX_1['EC_NETs_1'][i] * model.G[i]) + (AFF * model.EC_NET_1[i] * model.CPX_2['EC_NETs_1'][i]) + + (model.EC_NET_2[i] * model.NET_COST['EC_NETs_2'][i]) + (AFF * model.CPX_1['EC_NETs_2'][i] * model.H[i]) + (AFF * model.EC_NET_2[i] * model.CPX_2['EC_NETs_2'][i]) + + (model.EC_NET_3[i] * model.NET_COST['EC_NETs_3'][i]) + (AFF * model.CPX_1['EC_NETs_3'][i] * model.I[i]) + (AFF * model.EC_NET_3[i] * model.CPX_2['EC_NETs_3'][i]) + + (model.EP_NET_1[i] * model.NET_COST['EP_NETs_1'][i]) + (AFF * model.CPX_1['EP_NETs_1'][i] * model.D[i]) + (AFF * model.EP_NET_1[i] * model.CPX_2['EP_NETs_1'][i]) + + (model.EP_NET_2[i] * model.NET_COST['EP_NETs_2'][i]) + (AFF * model.CPX_1['EP_NETs_2'][i] * model.E[i]) + (AFF * model.EP_NET_2[i] * model.CPX_2['EP_NETs_2'][i]) + + (model.EP_NET_3[i] * model.NET_COST['EP_NETs_3'][i]) + (AFF * model.CPX_1['EP_NETs_3'][i] * model.F[i]) + (AFF * model.EP_NET_3[i] * model.CPX_2['EP_NETs_3'][i]) + + (model.REN_SOLAR[i] * model.REN_COST['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.J[i]) + (AFF * model.REN_SOLAR[i] * model.CPX_2['SOLAR'][i]) + + (model.REN_HYDRO[i] * model.REN_COST['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.K[i]) + (AFF * model.REN_HYDRO[i] * model.CPX_2['HYDRO'][i]) + + (model.REN_BM[i] * model.REN_COST['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.L[i]) + (AFF * model.REN_BM[i] * model.CPX_2['BIOMASS'][i]) + + (model.REN_BG[i] * model.REN_COST['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.M[i]) + (AFF * model.REN_BG[i] * model.CPX_2['BIOGAS'][i]) + + (model.REN_MSW[i] * model.REN_COST['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.N[i]) + (AFF * model.REN_MSW[i] * model.CPX_2['MSW'][i]) + + sum(model.energy_cost[i,s] for s in model.S) + sum(model.CT_cost[i,s] for s in model.S) - sum(model.CCS_selling_price[i,s] for s in model.S) == model.sum_cost[i]) + + model.Cons_97 = pyo.Constraint(i, model.S, rule = sum_cost) + + + def Total_profit (model, i, s): + return (sum(model.Plant_earning[i,s] for s in model.S)) - (model.sum_cost[i]) == model.total_profit[i] + + model.Cons_98 = pyo.Constraint(i, model.S, rule = Total_profit) + + + #This constraint ensures the net profitability over period i + def Plant_profit_net (model, i, s): + if Plant_Profit == 'ON': + return model.Plant_cost[i,s] >= 0 + elif Plant_Profit == 'OFF': + return pyo.Constraint.Skip + + model.Cons_99 = pyo.Constraint(i, model.S, rule = Plant_profit_net) + + + #For the minimum budget objective function, the total cost is minimised, subject to the satisfaction of the CO2 emission limit + #For the minimum emission objective function, the total emission is minimised, subject to the available budgetary constraint + def objective_constraint(model, i): + if flag == 'min_budget': + return model.new_emission[i] <= model.EP['Limit'][i] + elif flag == 'min_emission': + return model.sum_cost[i] <= model.EP['Budget'][i] + else: + return pyo.Constraint.Skip + + model.Cons_100 = pyo.Constraint(i, rule = objective_constraint) + + + #Constraints 101 and 102 are to check the feasibility of the model, and it also performs maximization of profits + def objective_constraint(model, i): + if flag == 'max_profit': + return model.new_emission[i] <= model.EP['Limit'][i] + else: + return pyo.Constraint.Skip + + model.Cons_101 = pyo.Constraint(i, rule = objective_constraint) + + + def objective_constraint(model, i): + if flag == 'max_profit': + return model.sum_cost[i] <= model.EP['Budget'][i] + else: + return pyo.Constraint.Skip + + model.Cons_102 = pyo.Constraint(i, rule = objective_constraint) + + + #opt = SolverFactory('octeract-engine', tee = True) + #results = opt.solve(model) + + opt = SolverFactory('gams') + #sys.exit() + + results = opt.solve(model, solver = 'scip', tee = True, add_options = ['option reslim = 36000;', 'option optcr = 0.01;', 'option optca = 0.01;', 'GAMS_MODEL.nodlim = 1000000;']) + #optca: Absolute optimality criterion for MIPs, and is of the order of 10^-9 by default + #optcr: This option specifies a relative termination tolerance for use in solving MIP problems, default is 5E-8, set it between 0.01 and 0.05 for quick computation, having a low value for this gives better results + + #opt = SolverFactory('gurobi') + #results = opt.solve(model, tee = True) + + print(results) + #model.pprint() + return model + +def multiperiod_energy_planning_results(model, i): + energy_planning = pd.DataFrame() + + for s in model.plant.keys(): + energy_planning.loc[s, 'Fuel'] = model.plant[s]['Fuel'] + energy_planning.loc[s, 'Energy Generation'] = model.A[i,s]() + energy_planning.loc[s, 'Gross Energy (TWh/y)'] = round(model.energy[i,s](), 3) + energy_planning.loc[s, 'CO2 Intensity (Mt/TWh)'] = model.plant[s]['CI'] + energy_planning.loc[s, 'CCS_1 CI'] = round(model.CI_RET_1[i,s](), 3) + energy_planning.loc[s, 'CCS_2 CI'] = round(model.CI_RET_2[i,s](), 3) + energy_planning.loc[s, 'CCS_1 Selection'] = model.B[i,s]() + energy_planning.loc[s, 'CCS_1 Ret (TWh/y)'] = round(model.CCS_1[i,s](), 3) + energy_planning.loc[s, 'CCS_2 Selection'] = model.C[i,s]() + energy_planning.loc[s, 'CCS_2 Ret (TWh/y)'] = round(model.CCS_2[i,s](), 3) + energy_planning.loc[s, 'Net Energy wo CCS'] = round(model.net_energy[i,s](), 3) + energy_planning.loc[s, 'Net Energy w CCS_1'] = round(model.net_energy_CCS_1[i,s](), 3) + energy_planning.loc[s, 'Net Energy w CCS_2'] = round(model.net_energy_CCS_2[i,s](), 3) + energy_planning.loc[s, 'Solid_1 Selection'] = model.O[i,s]() + energy_planning.loc[s, 'SOLID_1 (TWh/y)'] = round(model.solid_1[i,s](), 3) + energy_planning.loc[s, 'Solid_2 Selection'] = model.P[i,s]() + energy_planning.loc[s, 'SOLID_2 (TWh/y)'] = round(model.solid_2[i,s](), 3) + energy_planning.loc[s, 'Gas_1 Selection'] = model.Q[i,s]() + energy_planning.loc[s, 'GAS_1 (TWh/y)'] = round(model.gas_1[i,s](), 3) + energy_planning.loc[s, 'Gas_2 Selection'] = model.R[i,s]() + energy_planning.loc[s, 'GAS_2 (TWh/y)'] = round(model.gas_2[i,s](), 3) + energy_planning.loc[s, 'Net Energy (TWh/y)'] = round(model.net_energy[i,s]() + model.net_energy_CCS_1[i,s]() + model.net_energy_CCS_2[i,s]() + model.solid_1[i,s]() + model.solid_2[i,s]() + model.gas_1[i,s]() + model.gas_2[i,s](), 3) + energy_planning.loc[s, 'CO2 Load (Mt/y)'] = round(model.net_plant_emissions[i,s](), 3) + #Below lines are commented by the if/else statement if CT is turned off + if CT == 'ON': + energy_planning.loc[s, 'CT Emissions Price (mil USD / Mt)'] = round(model.emissions_unit_cost[i,s](), 3) + energy_planning.loc[s, 'Emissions bought from previous period (Mt/y)'] = round(model.ET_plant_new[i,s](), 3) + energy_planning.loc[s, 'Emission Rights Bought by Each Plant from government (Mt/y)'] = round(model.CT_emissions [i,s](), 3) + energy_planning.loc[s, 'Emissions rights available for trading within companies (Mt/y)'] = round(model.ET_plant[i,s](), 3) + energy_planning.loc[s, 'Emissions traded within a company (Mt/y)'] = round(model.ET_plant_final[i,s](), 3) + energy_planning.loc[s, 'Net Emissions traded within a company (Mt/y)'] = round(((model.ET_plant[i,s]()) - (model.ET_plant_final[i,s]())), 3) + energy_planning.loc[s, 'CT Cost Paid to government to buy emissions from previous period (mil USD / y)'] = round((model.ET_plant_new[i,s]() * model.emissions_unit_cost[i,s]()), 3) + energy_planning.loc[s, 'CT Cost Paid by Plant to government (mil USD / y)'] = round((model.CT_emissions [i,s]() * model.emissions_unit_cost[i,s]()), 3) + energy_planning.loc[s, 'CT Cost Paid/earned by each plant as a result of trading with each other (mil USD / y)'] = round(model.emissions_unit_cost[i,s]() * model.ET_plant_final [i,s](), 3) + energy_planning.loc[s, 'Balance Emissions to be traded in next period'] = round(model.Emissions_left[i,s](), 3) + energy_planning.loc[s, 'Price paid/earned for balance emissions (mil USD / y)'] = round((model.Emissions_left[i,s]() * model.emissions_unit_cost[i,s]()), 3) + else: + pass + #Above lines are commented by the if/else statement if CT is turned off + energy_planning.loc[s, 'Cumulative total fuel and annualized capital cost (mil USD/y)'] = round(model.energy_cost[i,s](), 3) + energy_planning.loc[s, 'Annualized capital cost (mil USD/y)'] = round(model.annualized_capital_cost[i,s](), 3) + energy_planning.loc[s, 'Fuel cost (mil USD/y)'] = round(model.fuel_cost[i,s](), 3) + energy_planning.loc[s, 'Cost contribution of CCS_1 (mil USD/y)'] = round((model.net_energy_CCS_1[i,s]() * model.CCS_data['Cost_CCS_1'][i]) + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]()), 3) + energy_planning.loc[s, 'Cost contribution of CCS_2 (mil USD/y)'] = round((model.net_energy_CCS_2[i,s]() * model.CCS_data['Cost_CCS_2'][i]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]()), 3) + energy_planning.loc[s, 'Cost contribution of Solid_1 (mil USD/y)'] = round((model.solid_1[i,s]() * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]()), 3) + energy_planning.loc[s, 'Cost contribution of Solid_2 (mil USD/y)'] = round((model.solid_2[i,s]() * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]()), 3) + energy_planning.loc[s, 'Cost contribution of Gas_1 (mil USD/y)'] = round((model.gas_1[i,s]() * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]()), 3) + energy_planning.loc[s, 'Cost contribution of Gas_2 (mil USD/y)'] = round((model.gas_2[i,s]() * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]()), 3) + energy_planning.loc[s, 'CO2 Sold by Plant (Mt/y)'] = round((((model.net_energy_CCS[i,s]())*(model.plant[s]['CI'])) - ((model.net_energy_CCS_1[i,s]())*(model.CI_RET_1[i,s]())) - ((model.net_energy_CCS_2[i,s]())*(model.CI_RET_2[i,s]()))), 3) + energy_planning.loc[s, 'Earning by CO2 selling (mil USD / y)'] = round(model.CCS_selling_price[i,s](), 3) + energy_planning.loc[s, 'Electricty Prices (mil USD / TWh)'] = round(model.Electricity_price[i,s]() * model.A[i,s](), 3) + energy_planning.loc[s, 'Earning by selling electricty (mil USD / y)'] = round(model.Plant_earning[i,s](), 3) + energy_planning.loc[s, 'Plant Profit (mil USD/y)'] = round(model.Plant_cost[i,s](), 3) + energy_planning.loc[s, 'Total Cost (mil USD/y)'] = round((model.Plant_earning[i,s]()) - (model.Plant_cost[i,s]()), 3) + + energy_planning.loc['EP_NET_1', 'Fuel'] = 'EP_NET_1' + energy_planning.loc['EP_NET_2', 'Fuel'] = 'EP_NET_2' + energy_planning.loc['EP_NET_3', 'Fuel'] = 'EP_NET_3' + energy_planning.loc['EC_NET_1', 'Fuel'] = 'EC_NET_1' + energy_planning.loc['EC_NET_2', 'Fuel'] = 'EC_NET_2' + energy_planning.loc['EC_NET_3', 'Fuel'] = 'EC_NET_3' + energy_planning.loc['EP_NET_1', 'Energy Generation'] = model.D[i]() + energy_planning.loc['EP_NET_2', 'Energy Generation'] = model.E[i]() + energy_planning.loc['EP_NET_3', 'Energy Generation'] = model.F[i]() + energy_planning.loc['EC_NET_1', 'Energy Generation'] = model.G[i]() + energy_planning.loc['EC_NET_2', 'Energy Generation'] = model.H[i]() + energy_planning.loc['EC_NET_3', 'Energy Generation'] = model.I[i]() + energy_planning.loc['EP_NET_1', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_1'][i] + energy_planning.loc['EP_NET_2', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_2'][i] + energy_planning.loc['EP_NET_3', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_3'][i] + energy_planning.loc['EC_NET_1', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_1'][i] + energy_planning.loc['EC_NET_2', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_2'][i] + energy_planning.loc['EC_NET_3', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_3'][i] + energy_planning.loc['EP_NET_1', 'Net Energy (TWh/y)'] = round(model.EP_NET_1[i](), 3) + energy_planning.loc['EP_NET_2', 'Net Energy (TWh/y)'] = round(model.EP_NET_2[i](), 3) + energy_planning.loc['EP_NET_3', 'Net Energy (TWh/y)'] = round(model.EP_NET_3[i](), 3) + energy_planning.loc['EC_NET_1', 'Net Energy (TWh/y)'] = round(model.EC_NET_1[i](), 3) + energy_planning.loc['EC_NET_2', 'Net Energy (TWh/y)'] = round(model.EC_NET_2[i](), 3) + energy_planning.loc['EC_NET_3', 'Net Energy (TWh/y)'] = round(model.EC_NET_3[i](), 3) + energy_planning.loc['EP_NET_1', 'CO2 Load (Mt/y)'] = round(model.EP_NET_1[i]() * model.NET_CI['EP_NETs_1'][i], 3) + energy_planning.loc['EP_NET_2', 'CO2 Load (Mt/y)'] = round(model.EP_NET_2[i]() * model.NET_CI['EP_NETs_2'][i], 3) + energy_planning.loc['EP_NET_3', 'CO2 Load (Mt/y)'] = round(model.EP_NET_3[i]() * model.NET_CI['EP_NETs_3'][i], 3) + energy_planning.loc['EC_NET_1', 'CO2 Load (Mt/y)'] = round(model.EC_NET_1[i]() * model.NET_CI['EC_NETs_1'][i], 3) + energy_planning.loc['EC_NET_2', 'CO2 Load (Mt/y)'] = round(model.EC_NET_2[i]() * model.NET_CI['EC_NETs_2'][i], 3) + energy_planning.loc['EC_NET_3', 'CO2 Load (Mt/y)'] = round(model.EC_NET_3[i]() * model.NET_CI['EC_NETs_3'][i], 3) + energy_planning.loc['SOLAR', 'Fuel'] = 'SOLAR' + energy_planning.loc['HYDRO', 'Fuel'] = 'HYDRO' + energy_planning.loc['BIOMASS', 'Fuel'] = 'BIOMASS' + energy_planning.loc['BIOGAS', 'Fuel'] = 'BIOGAS' + energy_planning.loc['MSW', 'Fuel'] = 'MSW' + energy_planning.loc['SOLAR', 'Energy Generation'] = model.J[i]() + energy_planning.loc['HYDRO', 'Energy Generation'] = model.K[i]() + energy_planning.loc['BIOMASS', 'Energy Generation'] = model.L[i]() + energy_planning.loc['BIOGAS', 'Energy Generation'] = model.M[i]() + energy_planning.loc['MSW', 'Energy Generation'] = model.N[i]() + energy_planning.loc['SOLAR', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['SOLAR'][i] + energy_planning.loc['HYDRO', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['HYDRO'][i] + energy_planning.loc['BIOMASS', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['BIOMASS'][i] + energy_planning.loc['BIOGAS', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['BIOGAS'][i] + energy_planning.loc['MSW', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['MSW'][i] + energy_planning.loc['SOLAR', 'Net Energy (TWh/y)'] = round(model.REN_SOLAR[i](), 3) + energy_planning.loc['SOLAR', 'CO2 Load (Mt/y)'] = round(model.REN_SOLAR[i]() * model.REN_CI['SOLAR'][i], 3) + energy_planning.loc['HYDRO', 'Net Energy (TWh/y)'] = round(model.REN_HYDRO[i](), 3) + energy_planning.loc['HYDRO', 'CO2 Load (Mt/y)'] = round(model.REN_HYDRO[i]() * model.REN_CI['HYDRO'][i], 3) + energy_planning.loc['BIOMASS', 'Net Energy (TWh/y)'] = round(model.REN_BM[i](), 3) + energy_planning.loc['BIOMASS', 'CO2 Load (Mt/y)'] = round(model.REN_BM[i]() * model.REN_CI['BIOMASS'][i], 3) + energy_planning.loc['BIOGAS', 'Net Energy (TWh/y)'] = round(model.REN_BG[i](), 3) + energy_planning.loc['BIOGAS', 'CO2 Load (Mt/y)'] = round(model.REN_BG[i]() * model.REN_CI['BIOGAS'][i], 3) + energy_planning.loc['MSW', 'Net Energy (TWh/y)'] = round(model.REN_MSW[i](), 3) + energy_planning.loc['MSW', 'CO2 Load (Mt/y)'] = round(model.REN_MSW[i]() * model.REN_CI['MSW'][i], 3) + energy_planning.loc['TOTAL', 'CO2 Load (Mt/y)'] = round(model.new_emission[i](), 3) + energy_planning.loc['EC_NET_1', 'Total Cost (mil USD/y)'] = round(sum(((model.EC_NET_1[i]() * model.NET_COST['EC_NETs_1'][i]) + (AFF * model.CPX_1['EC_NETs_1'][i] * model.G[i]()) + (AFF * model.EC_NET_1[i]() * model.CPX_2['EC_NETs_1'][i])) for s in model.S), 3) + energy_planning.loc['EC_NET_2', 'Total Cost (mil USD/y)'] = round(sum(((model.EC_NET_2[i]() * model.NET_COST['EC_NETs_2'][i]) + (AFF * model.CPX_1['EC_NETs_2'][i] * model.H[i]()) + (AFF * model.EC_NET_2[i]() * model.CPX_2['EC_NETs_2'][i])) for s in model.S), 3) + energy_planning.loc['EC_NET_3', 'Total Cost (mil USD/y)'] = round(sum(((model.EC_NET_3[i]() * model.NET_COST['EC_NETs_3'][i]) + (AFF * model.CPX_1['EC_NETs_3'][i] * model.I[i]()) + (AFF * model.EC_NET_3[i]() * model.CPX_2['EC_NETs_3'][i])) for s in model.S), 3) + energy_planning.loc['EP_NET_1', 'Total Cost (mil USD/y)'] = round(sum(((model.EP_NET_1[i]() * model.NET_COST['EP_NETs_1'][i]) + (AFF * model.CPX_1['EP_NETs_1'][i] * model.D[i]()) + (AFF * model.EP_NET_1[i]() * model.CPX_2['EP_NETs_1'][i])) for s in model.S), 3) + energy_planning.loc['EP_NET_2', 'Total Cost (mil USD/y)'] = round(sum(((model.EP_NET_2[i]() * model.NET_COST['EP_NETs_2'][i]) + (AFF * model.CPX_1['EP_NETs_2'][i] * model.E[i]()) + (AFF * model.EP_NET_2[i]() * model.CPX_2['EP_NETs_2'][i])) for s in model.S), 3) + energy_planning.loc['EP_NET_3', 'Total Cost (mil USD/y)'] = round(sum(((model.EP_NET_3[i]() * model.NET_COST['EP_NETs_3'][i]) + (AFF * model.CPX_1['EP_NETs_3'][i] * model.F[i]()) + (AFF * model.EP_NET_3[i]() * model.CPX_2['EP_NETs_3'][i])) for s in model.S), 3) + energy_planning.loc['SOLAR', 'Total Cost (mil USD/y)'] = round(sum(((model.REN_SOLAR[i]() * model.REN_COST['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.J[i]()) + (AFF * model.REN_SOLAR[i]() * model.CPX_2['SOLAR'][i])) for s in model.S), 3) + energy_planning.loc['HYDRO', 'Total Cost (mil USD/y)'] = round(sum(((model.REN_HYDRO[i]() * model.REN_COST['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.K[i]()) + (AFF * model.REN_HYDRO[i]() * model.CPX_2['HYDRO'][i])) for s in model.S), 3) + energy_planning.loc['BIOMASS', 'Total Cost (mil USD/y)'] = round(sum(((model.REN_BM[i]() * model.REN_COST['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.L[i]()) + (AFF * model.REN_BM[i]() * model.CPX_2['BIOMASS'][i])) for s in model.S), 3) + energy_planning.loc['BIOGAS', 'Total Cost (mil USD/y)'] = round(sum(((model.REN_BG[i]() * model.REN_COST['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.M[i]()) + (AFF * model.REN_BG[i]() * model.CPX_2['BIOGAS'][i])) for s in model.S), 3) + energy_planning.loc['MSW', 'Total Cost (mil USD/y)'] = round(sum(((model.REN_MSW[i]() * model.REN_COST['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.N[i]()) + (AFF * model.REN_MSW[i]() * model.CPX_2['MSW'][i])) for s in model.S), 3) + #Below lines are commented by the if/else statement if CT is turned off + if CT == 'ON': + energy_planning.loc['TOTAL', 'Emissions bought from previous period (Mt/y)'] = round(sum(model.ET_plant_new [i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Emission Rights Bought by Each Plant from government (Mt/y)'] = round(sum(model.CT_emissions [i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Emissions rights available for trading within companies (Mt/y)'] = round(sum(model.ET_plant[i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Emissions traded within a company (Mt/y)'] = round(sum(model.ET_plant_final [i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'CT Cost Paid to government to buy emissions from previous period (mil USD / y)'] = round(sum((model.ET_plant_new[i,s]() * model.emissions_unit_cost[i,s]())for s in model.S) , 3) + energy_planning.loc['TOTAL', 'CT Cost Paid by Plant to government (mil USD / y)'] = round(sum((model.CT_emissions [i,s]() * model.emissions_unit_cost[i,s]()) for s in model.S), 3) + energy_planning.loc['TOTAL', 'CT Cost Paid/earned by each plant as a result of trading with each other (mil USD / y)'] = round(sum((model.emissions_unit_cost[i,s]() * model.ET_plant_final [i,s]()) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Balance Emissions to be traded in next period'] = round(model.CT_balance_emissions[i](), 3) + energy_planning.loc['TOTAL', 'Price paid/earned for balance emissions (mil USD / y)'] = round(sum((model.Emissions_left[i,s]() * model.emissions_unit_cost[i,s]()) for s in model.S) , 3) + else: + pass + #Above lines are commented by the if/else statement if CT is turned off + energy_planning.loc['TOTAL', 'Cumulative total fuel and annualized capital cost (mil USD/y)'] = round(sum(model.energy_cost[i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Annualized capital cost (mil USD/y)'] = round(sum(model.annualized_capital_cost[i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Fuel cost (mil USD/y)'] = round(sum(model.fuel_cost[i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Cost contribution of CCS_1 (mil USD/y)'] = round(sum(((model.net_energy_CCS_1[i,s]() * model.CCS_data['Cost_CCS_1'][i]) + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]())) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Cost contribution of CCS_2 (mil USD/y)'] = round(sum(((model.net_energy_CCS_2[i,s]() * model.CCS_data['Cost_CCS_2'][i]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]())) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Cost contribution of Solid_1 (mil USD/y)'] = round(sum(((model.solid_1[i,s]() * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]())) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Cost contribution of Solid_2 (mil USD/y)'] = round(sum(((model.solid_2[i,s]() * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]())) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Cost contribution of Gas_1 (mil USD/y)'] = round(sum(((model.gas_1[i,s]() * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]())) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Cost contribution of Gas_2 (mil USD/y)'] = round(sum(((model.gas_2[i,s]() * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]())) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Earning by CO2 selling (mil USD / y)'] = round(sum(model.CCS_selling_price[i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Earning by selling electricty (mil USD / y)'] = round(sum(model.Plant_earning[i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Plant Profit (mil USD/y)'] = round(model.total_profit[i](), 3) + energy_planning.loc['TOTAL', 'Total Cost (mil USD/y)'] = round(model.sum_cost[i](), 3) + + writer = pd.ExcelWriter(file_name, engine = 'openpyxl', mode = 'a', if_sheet_exists = 'new') + #writer = pd.ExcelWriter(file_name, mode = 'a', if_sheet_exists = 'new') + energy_planning.to_excel(writer, sheet_name = 'Results_Period_1') + writer.save() + + return + + +model = multiperiod_energy_planning(model, periods) + +emissions_each_period = [] +for i in periods: + emissions_each_period.append(model.new_emission[i].value) + +cost_each_period = [] +for i in periods: + cost_each_period.append(model.sum_cost[i].value) + +profit_each_period = [] +for i in periods: + profit_each_period.append(((sum((model.Plant_earning[i,s].value) for s in model.S)) - (model.sum_cost[i].value))) + + +wb = load_workbook(file_name) +sheet_results = wb['FINAL_RESULTS'] + +df = pd.DataFrame({'Emissions': emissions_each_period}) + +for index, row in df.iterrows(): + cell = 'B%d' % (index + 2) + sheet_results[cell] = row[0] + +wb.save(file_name) + + +df_0 = pd.DataFrame({'Cost': cost_each_period}) + +for index0, row0 in df_0.iterrows(): + cell0 = 'C%d' % (index0 + 2) + sheet_results[cell0] = row0[0] + +wb.save(file_name) + +df_1 = pd.DataFrame({'Profit': profit_each_period}) + +for index1, row1 in df_1.iterrows(): + cell1 = 'D%d' % (index1 + 2) + sheet_results[cell1] = row1[0] + +wb.save(file_name) + +#Below lines print/display the results in excel-based user interface +if Print_Result == 'YES': + for i in list(range(1, numperiods,1)): + multiperiod_energy_planning_results(model,i) +else: + pass diff --git a/Direct Optimization Model/DO_EP_Optimal_Decarbonisation_Run_File.py b/Direct Optimization Model/DO_EP_Optimal_Decarbonisation_Run_File.py new file mode 100644 index 0000000..4bf451d --- /dev/null +++ b/Direct Optimization Model/DO_EP_Optimal_Decarbonisation_Run_File.py @@ -0,0 +1,20 @@ +''' +Created on 24th September 2021 +Last Modified in March 2023 + +This script presents the mathematical optimisation formulation for an energy planning scenario in a specific geographical region or district. +Renewable energy and fossil-based sources make up the power generation in a specific geographical region or district. +Each period consists of its respective energy demand and CO2 emission limit. +EP-NETs, EC-NETs, CCS/CCSU and Carbon Trading may be utilised to achieve the CO2 emission limit for each period. +This energy planning mathematical formulation may be optimised subject to either a mininum cost or minimum emission objective function. +There is an addtional formulation to optimize for maximum profit which satisfies both emission targets and budget contraints, which proves the feasibility of data/model, and provides effective initial values. + +@author: Gul, Purusothmn, Dr Michael Short, Prof Dr Dominic C.Y.F. +''' +import os + +path = os.chdir(r'C:\Users\pc\Desktop\DECO2 v2023\Direct Optimization Model') + +cwd = os.getcwd() + +import DO_EP_Optimal_Decarbonisation_Model_Python diff --git a/Direct Optimization Model/LICENSE b/Direct Optimization Model/LICENSE new file mode 100644 index 0000000..0ad25db --- /dev/null +++ b/Direct Optimization Model/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Please read the user manual in the main folder for information on the formulation and how the inputs work. For details on the partnership, please visit our site: + +https://www.surrey.ac.uk/research-projects/software-framework-optimal-decarbonisation-planning-asean-countries diff --git a/Direct Optimization Model/__pycache__/IDO_Base_Model_Python.cpython-38.pyc b/Direct Optimization Model/__pycache__/IDO_Base_Model_Python.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7df2ee5cda9781f8e6559a1161b2f142a52685f2 GIT binary patch literal 58687 zcmeHw3w&Hhb@#rslGd^;+p=scj3( z-MjKCazgt1A^q;`+;h&HnKO?wXP)=K?AeJB{(kb=za4t#-Qm#R(Tn+4ig7n25Z(p=jPzINyG=|TD3>#S#t%2tP}Mj@kq_N`R7AcQ@UAVW&u+#8Xk#B_yP%^ z4S1s`M2p1C0j9|au~5S20zS`xFOu;2KKNn@Zw7pULEkFjEk5{V621`dMF#y63194k zUoPRTKKN1zzsv`}Lc*5-ZpxLE@XLMhD}!K>8T(ejx14;UabaliTD+}b_-gQJ2A?(78gUIn z)>_wD>qMh32JywswW5?i5(P8b7Ja$<-C68Uq z;}#!}_15*wW4E>2;Bm{~tzv_9i{#O3-70zXGLJ_M9-jP%6ivvP4&?cUGZEx!^q#MY zNPnnpW9G@4P$HxhHcnnOc`hC3-W3VscMjWQVgrXLHG0GP#M2 zEwPE7oMn%kOzs=a*!2jgEr%;gd-#i!|Cv8_^uk93Hh)e1-gkJ!BP<*4mM^tCNNNXvSWhOdL5W?EE;f 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lower_bound_energy) + + + #The deployment of energy source in power plant s should at most satisfy the upper bound for period i + def upper_bound_energy(model, i, s): + return model.energy[i,s] <= model.plant[s]['UB'] * model.A[i,s] + + model.Cons_3 = pyo.Constraint(i, model.S, rule = upper_bound_energy) + + + #There should not be power generation from power plant s before its commissioning period + def plant_commission(model, i, s): + if i < model.plant[s]['ON']: + return model.energy[i,s] == 0 + else: + return model.energy[i,s] >= 0 + + model.Cons_4 = pyo.Constraint(i, model.S, rule = plant_commission) + + + #There should be power generation from power plant s after its decommissioning period + def plant_decommission(model, i, s): + if i >= model.plant[s]['OFF']: + return model.energy[i,s] == 0 + else: + return model.energy[i,s] >= 0 + + model.Cons_5 = pyo.Constraint(i, model.S, rule = plant_decommission) + + + #If power plant s is decomissioned in a period, it should remain decommissioned at later periods + #However, power plant s should be available for power generation till the period before its decommissioning period + def energy_constraint(model, i, s): + if i <= model.plant[s]['OFF'] - 2: + return model.energy[i+1,s] >= model.energy[i,s] + else: + return pyo.Constraint.Skip + + model.Cons_6 = pyo.Constraint(i, model.S, rule = energy_constraint) + + + #Calculation of carbon intensity of energy sources with CCS technology 1 in power plant s for period i + def CCS_CI_1(model, i, s): + return model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i]) == model.CI_RET_1[i,s] + + model.Cons_7 = pyo.Constraint(i, model.S, rule = CCS_CI_1) + + + #Calculation of carbon intensity of energy sources with CCS technology 2 in power plant s for period i + def CCS_CI_2(model, i, s): + return model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i]) == model.CI_RET_2[i,s] + + model.Cons_8 = pyo.Constraint(i, model.S, rule = CCS_CI_2) + + + #If selected, the deployment of CCS technology 1 in power plant s is limited by the upper bound of the energy output for period i + def CCS_limit_1(model, i, s): + return model.CCS_1[i,s] <= model.plant[s]['UB'] * model.B[i,s] + + model.Cons_9 = pyo.Constraint(i, model.S, rule = CCS_limit_1) + + + #If selected, the deployment of CCS technology 2 in power plant s is limited by the upper bound of the energy output for period i + def CCS_limit_2(model, i, s): + return model.CCS_2[i,s] <= model.plant[s]['UB'] * model.C[i,s] + + model.Cons_10 = pyo.Constraint(i, model.S, rule = CCS_limit_2) + + + #The total CCS deployment in power plant s should be equal to summation of deployment of individual types of CCS technology for period i + def CCS_total(model, i, s): + return model.CCS_1[i,s] + model.CCS_2[i,s] <= model.energy[i,s] + + model.Cons_11 = pyo.Constraint(i, model.S, rule = CCS_total) + + + #The deployment of CCS technology 1 at later periods should at least match the deployment in the previous period + def CCS_1_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.CCS_1[i+1,s] >= model.CCS_1[i,s] + + model.Cons_12 = pyo.Constraint(i, model.S, rule = CCS_1_constraint) + + + #The deployment of CCS technology 2 at later periods should at least match the deployment in the previous period + def CCS_2_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.CCS_2[i+1,s] >= model.CCS_2[i,s] + + model.Cons_13 = pyo.Constraint(i, model.S, rule = CCS_2_constraint) + + + #Determine the net energy available from power plant s with CCS technology 1 for period i + def CCS_1_net_energy(model, i, s): + return model.CCS_1[i,s] * (1 - model.CCS_data['X_1'][i]) == model.net_energy_CCS_1[i,s] + + model.Cons_14 = pyo.Constraint(i, model.S, rule = CCS_1_net_energy) + + + #Determine the net energy available from power plant s with CCS technology 2 for period i + def CCS_2_net_energy(model, i, s): + return model.CCS_2[i,s] * (1 - model.CCS_data['X_2'][i]) == model.net_energy_CCS_2[i,s] + + model.Cons_15 = pyo.Constraint(i, model.S, rule = CCS_2_net_energy) + + + #The deployment of alternative solid fuel technology 1 at later periods should at least match the deployment in the previous period + def alt_solid_1_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.solid_1[i+1,s] >= model.solid_1[i,s] + + model.Cons_16 = pyo.Constraint(i, model.S, rule = alt_solid_1_constraint) + + + #The deployment of alternative solid fuel technology 2 at later periods should at least match the deployment in the previous period + def alt_solid_2_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.solid_2[i+1,s] >= model.solid_2[i,s] + + model.Cons_17 = pyo.Constraint(i, model.S, rule = alt_solid_2_constraint) + + + #The deployment of alternative gas fuel technology 1 at later periods should at least match the deployment in the previous period + def alt_gas_1_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.gas_1[i+1,s] >= model.gas_1[i,s] + + model.Cons_18 = pyo.Constraint(i, model.S, rule = alt_gas_1_constraint) + + + #The deployment of alternative gas fuel technology 2 at later periods should at least match the deployment in the previous period + def alt_gas_2_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.gas_2[i+1,s] >= model.gas_2[i,s] + + model.Cons_19 = pyo.Constraint(i, model.S, rule = alt_gas_2_constraint) + + + #The total energy contribution must equal the initially determined energy contribution of power plant s for period i + def fuel_substitution(model, i, s): + if 'REN' in model.plant[s].values(): + return model.net_energy[i,s] == model.energy[i,s] + elif 'NUCLEAR' in model.plant[s].values(): + return model.net_energy[i,s] == model.energy[i,s] + elif 'NG' in model.plant[s].values(): + return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] + model.gas_1[i,s] + model.gas_2[i,s] == model.energy[i,s] + elif 'COAL' in model.plant[s].values(): + return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] == model.energy[i,s] + else: + return model.net_energy[i,s] + model.CCS_1[i,s] + model.CCS_2[i,s] == model.energy[i,s] + + model.Cons_20 = pyo.Constraint(i, model.S, rule = fuel_substitution) + + + #If power plant s is fuelled by renewable energy sources, CCS technology 1 would not be deployed for period i + def no_CCS_1(model, i, s): + if 'REN' in model.plant[s].values(): + return model.CCS_1[i,s] == 0 + elif 'NUCLEAR' in model.plant[s].values(): + return model.CCS_1[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_21 = pyo.Constraint(i, model.S, rule = no_CCS_1) + + + #If power plant s is fuelled by renewable energy sources, CCS technology 1 would not be deployed for period i + def no_CCS_2(model, i, s): + if 'REN' in model.plant[s].values(): + return model.CCS_2[i,s] == 0 + elif 'NUCLEAR' in model.plant[s].values(): + return model.CCS_2[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_22 = pyo.Constraint(i, model.S, rule = no_CCS_2) + + + #If power plant s is fuelled by renewable energy sources, alternative fuels would not be deployed for period i + def no_AF(model, i, s): + if 'REN' in model.plant[s].values(): + return model.gas_1[i,s] + model.gas_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] == 0 + elif 'NUCLEAR' in model.plant[s].values(): + return model.gas_1[i,s] + model.gas_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_23 = pyo.Constraint(i, model.S, rule = no_AF) + + + #If power plant s is fuelled by renewable energy sources, alternative fuels would not be deployed for period i + def no_AF_selection(model, i, s): + if 'REN' in model.plant[s].values(): + return model.O[i,s] + model.P[i,s] + model.Q[i,s] + model.R[i,s] == 0 + elif 'NUCLEAR' in model.plant[s].values(): + return model.O[i,s] + model.P[i,s] + model.Q[i,s] + model.R[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_24 = pyo.Constraint(i, model.S, rule = no_AF_selection) + + + #Technology implementation time for CCS technology 1 + def deployment_CCS_1(model, i, s): + if model.TIME['CCS_1'][i] == 'NO': + return model.B[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_25 = pyo.Constraint(i, model.S, rule = deployment_CCS_1) + + + #Technology implementation time for CCS technology 2 + def deployment_CCS_2(model, i, s): + if model.TIME['CCS_2'][i] == 'NO': + return model.C[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_26 = pyo.Constraint(i, model.S, rule = deployment_CCS_2) + + + #Technology implementation time for EP-NETs technology 1 + def deployment_EP_NETs_1(model, i): + if model.TIME['EP_NETs_1'][i] == 'NO': + return model.D[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_27 = pyo.Constraint(i, rule = deployment_EP_NETs_1) + + + #Technology implementation time for EP-NETs technology 2 + def deployment_EP_NETs_2(model, i): + if model.TIME['EP_NETs_2'][i] == 'NO': + return model.E[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_28 = pyo.Constraint(i, rule = deployment_EP_NETs_2) + + + #Technology implementation time for EP-NETs technology 3 + def deployment_EP_NETs_3(model, i): + if model.TIME['EP_NETs_3'][i] == 'NO': + return model.F[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_29 = pyo.Constraint(i, rule = deployment_EP_NETs_3) + + + #Technology implementation time for EC-NETs technology 1 + def deployment_EC_NETs_1(model, i): + if model.TIME['EC_NETs_1'][i] == 'NO': + return model.G[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_30 = pyo.Constraint(i, rule = deployment_EC_NETs_1) + + + #Technology implementation time for EC-NETs technology 2 + def deployment_EC_NETs_2(model, i): + if model.TIME['EC_NETs_2'][i] == 'NO': + return model.H[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_31 = pyo.Constraint(i, rule = deployment_EC_NETs_2) + + + #Technology implementation time for EC-NETs technology 3 + def deployment_EC_NETs_3(model, i): + if model.TIME['EC_NETs_3'][i] == 'NO': + return model.I[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_32 = pyo.Constraint(i, rule = deployment_EC_NETs_3) + + + #Technology implementation time for solar compensatory energy + def deployment_solar(model, i): + if model.TIME['SOLAR'][i] == 'NO': + return model.J[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_33 = pyo.Constraint(i, rule = deployment_solar) + + + #Technology implementation time for hydropower compensatory energy + def deployment_hydro(model, i): + if model.TIME['HYDRO'][i] == 'NO': + return model.K[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_34 = pyo.Constraint(i, rule = deployment_hydro) + + + #Technology implementation time for biomass compensatory energy + def deployment_biomass(model, i): + if model.TIME['BIOMASS'][i] == 'NO': + return model.L[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_35 = pyo.Constraint(i, rule = deployment_biomass) + + + #Technology implementation time for biogas compensatory energy + def deployment_biogas(model, i): + if model.TIME['BIOGAS'][i] == 'NO': + return model.M[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_36 = pyo.Constraint(i, rule = deployment_biogas) + + + #Technology implementation time for MSW compensatory energy + def deployment_MSW(model, i): + if model.TIME['MSW'][i] == 'NO': + return model.N[i] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_37 = pyo.Constraint(i, rule = deployment_MSW) + + + #Technology implementation time for alternative solid fuel type 1 + def deployment_alt_solid_1(model, i, s): + if model.TIME['SOLID_1'][i] == 'NO': + return model.O[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_38 = pyo.Constraint(i, model.S, rule = deployment_alt_solid_1) + + + #Technology implementation time for alternative solid fuel type 2 + def deployment_alt_solid_2(model, i, s): + if model.TIME['SOLID_2'][i] == 'NO': + return model.P[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_39 = pyo.Constraint(i, model.S, rule = deployment_alt_solid_2) + + + #Technology implementation time for alternative gas fuel type 1 + def deployment_alt_gas_1(model, i, s): + if model.TIME['GAS_1'][i] == 'NO': + return model.Q[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_40 = pyo.Constraint(i, model.S, rule = deployment_alt_gas_1) + + + #Technology implementation time for alternative gas fuel type 2 + def deployment_alt_gas_2(model, i, s): + if model.TIME['GAS_2'][i] == 'NO': + return model.R[i,s] == 0 + else: + return pyo.Constraint.Skip + + model.Cons_41 = pyo.Constraint(i, model.S, rule = deployment_alt_gas_2) + + + #Big M formulation for EP-NETs technology 1 deployment for period i + def big_M_EP_NETs_1(model, i): + return model.EP_NET_1[i] <= model.D[i] * 1000 + + model.Cons_42 = pyo.Constraint(i, rule = big_M_EP_NETs_1) + + + #Big M formulation for EP-NETs technology 2 deployment for period i + def big_M_EP_NETs_2(model, i): + return model.EP_NET_2[i] <= model.E[i] * 1000 + + model.Cons_43 = pyo.Constraint(i, rule = big_M_EP_NETs_2) + + + #Big M formulation for EP-NETs technology 3 deployment for period i + def big_M_EP_NETs_3(model, i): + return model.EP_NET_3[i] <= model.F[i] * 1000 + + model.Cons_44 = pyo.Constraint(i, rule = big_M_EP_NETs_3) + + + #Big M formulation for EC-NETs technology 1 deployment for period i + def big_M_EC_NETs_1(model, i): + return model.EC_NET_1[i] <= model.G[i] * 1000 + + model.Cons_45 = pyo.Constraint(i, rule = big_M_EC_NETs_1) + + + #Big M formulation for EC-NETs technology 2 deployment for period i + def big_M_EC_NETs_2(model, i): + return model.EC_NET_2[i] <= model.H[i] * 1000 + + model.Cons_46 = pyo.Constraint(i, rule = big_M_EC_NETs_2) + + + #Big M formulation for EC-NETs technology 3 deployment for period i + def big_M_EC_NETs_3(model, i): + return model.EC_NET_3[i] <= model.I[i] * 1000 + + model.Cons_47 = pyo.Constraint(i, rule = big_M_EC_NETs_3) + + + #Big M formulation for deployment of solar renewable energy for period i + def big_M_solar(model, i): + return model.REN_SOLAR[i] <= model.J[i] * 1000 + + model.Cons_48 = pyo.Constraint(i, rule = big_M_solar) + + + #Big M formulation for deployment of hydro renewable energy for period i + def big_M_hydro(model, i): + return model.REN_HYDRO[i] <= model.K[i] * 1000 + + model.Cons_49 = pyo.Constraint(i, rule = big_M_hydro) + + + #Big M formulation for deployment of BM renewable energy for period i + def big_M_biomass(model, i): + return model.REN_BM[i] <= model.L[i] * 1000 + + model.Cons_50 = pyo.Constraint(i, rule = big_M_biomass) + + + #Big M formulation for deployment of BG renewable energy for period i + def big_M_biogas(model, i): + return model.REN_BG[i] <= model.M[i] * 1000 + + model.Cons_51 = pyo.Constraint(i, rule = big_M_biogas) + + + #Big M formulation for deployment of MSW renewable energy for period i + def big_M_MSW(model, i): + return model.REN_MSW[i] <= model.N[i] * 1000 + + model.Cons_52 = pyo.Constraint(i, rule = big_M_MSW) + + + #Big M formulation for deployment of alternative solid fuel type 1 for period i + def big_M_alt_solid_1(model, i, s): + return model.solid_1[i,s] <= model.O[i,s] * model.plant[s]['UB'] + + model.Cons_53 = pyo.Constraint(i, model.S, rule = big_M_alt_solid_1) + + + #Big M formulation for deployment of alternative solid fuel type 2 for period i + def big_M_alt_solid_2(model, i, s): + return model.solid_2[i,s] <= model.P[i,s] * model.plant[s]['UB'] + + model.Cons_54 = pyo.Constraint(i, model.S, rule = big_M_alt_solid_2) + + + #Big M formulation for deployment of alternative gas fuel type 1 for period i + def big_M_alt_gas_1(model, i, s): + return model.gas_1[i,s] <= model.Q[i,s] * model.plant[s]['UB'] + + model.Cons_55 = pyo.Constraint(i, model.S, rule = big_M_alt_gas_1) + + + #Big M formulation for deployment of alternative gas fuel type 2 for period i + def big_M_alt_gas_2(model, i, s): + return model.gas_2[i,s] <= model.R[i,s] * model.plant[s]['UB'] + + model.Cons_56 = pyo.Constraint(i, model.S, rule = big_M_alt_gas_2) + + + #The deployment of solar renewable energy at later periods should at least match the deployment in the previous period + def solar_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.REN_SOLAR[i+1] >= model.REN_SOLAR[i] + + model.Cons_57 = pyo.Constraint(i, model.S, rule = solar_time_constraint) + + + #The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period + def hydro_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.REN_HYDRO[i+1] >= model.REN_HYDRO[i] + + model.Cons_58 = pyo.Constraint(i, model.S, rule = hydro_time_constraint) + + + #The deployment of solar renewable energy at later periods should at least match the deployment in the previous period + def biomass_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.REN_BM[i+1] >= model.REN_BM[i] + + model.Cons_59 = pyo.Constraint(i, model.S, rule = biomass_time_constraint) + + + #The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period + def biogas_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.REN_BG[i+1] >= model.REN_BG[i] + + model.Cons_60 = pyo.Constraint(i, model.S, rule = biogas_time_constraint) + + + #The deployment of hydro renewable energy at later periods should at least match the deployment in the previous period + def MSW_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.REN_MSW[i+1] >= model.REN_MSW[i] + + model.Cons_61 = pyo.Constraint(i, model.S, rule = MSW_time_constraint) + + + #The deployment of EP-NETs technology 1 at later periods should at least match the deployment in the previous period + def EP_NETs_1_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.EP_NET_1[i+1] >= model.EP_NET_1[i] + + model.Cons_62 = pyo.Constraint(i, model.S, rule = EP_NETs_1_time_constraint) + + + #The deployment of EP-NETs technology 2 at later periods should at least match the deployment in the previous period + def EP_NETs_2_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.EP_NET_2[i+1] >= model.EP_NET_2[i] + + model.Cons_63 = pyo.Constraint(i, model.S, rule = EP_NETs_2_time_constraint) + + + #The deployment of EP-NETs technology 3 at later periods should at least match the deployment in the previous period + def EP_NETs_3_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.EP_NET_3[i+1] >= model.EP_NET_3[i] + + model.Cons_64 = pyo.Constraint(i, model.S, rule = EP_NETs_3_time_constraint) + + + #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period + def EC_NETs_1_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.EC_NET_1[i+1] >= model.EC_NET_1[i] + + model.Cons_65 = pyo.Constraint(i, model.S, rule = EC_NETs_1_time_constraint) + + + #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period + def EC_NETs_2_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.EC_NET_2[i+1] >= model.EC_NET_2[i] + + model.Cons_66 = pyo.Constraint(i, model.S, rule = EC_NETs_2_time_constraint) + + + #The deployment of EC-NETs technology 1 at later periods should at least match the deployment in the previous period + def EC_NETs_3_time_constraint(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.EC_NET_3[i+1] >= model.EC_NET_3[i] + + model.Cons_67 = pyo.Constraint(i, model.S, rule = EC_NETs_3_time_constraint) + + + #Total energy contribution from all energy sources to satisfy the total demand for period i + def total_energy(model, i, s): + return sum((model.net_energy[i,s] + model.net_energy_CCS_1[i,s] + model.net_energy_CCS_2[i,s] + model.solid_1[i,s] + model.solid_2[i,s] + model.gas_1[i,s] + model.gas_2[i,s]) for s in model.S) + model.REN_SOLAR[i] + model.REN_HYDRO[i] + model.REN_BM[i] + model.REN_BG[i] + model.REN_MSW[i] + model.EP_NET_1[i] + model.EP_NET_2[i] + model.EP_NET_3[i] == model.EP['Demand'][i] + model.EC_NET_1[i] + model.EC_NET_2[i] + model.EC_NET_3[i] + + model.Cons_68 = pyo.Constraint(i, model.S, rule = total_energy) + + #Determine the net energy available from power plant s with the deployment of CCS technologies 1 & 2 for period i + def CCS_net_energy(model, i, s): + return model.net_energy_CCS_1[i,s] + model.net_energy_CCS_2[i,s] == model.net_energy_CCS[i,s] + + model.Cons_69 = pyo.Constraint(i, model.S, rule = CCS_net_energy) + + #Constraints below are related to carbon trading model + #This constraint forbids the non-generation plants to participate in CT + def Emissions_unit_cost_value(model, i, s): + if CT == 'ON': + return model.emissions_unit_cost_new[i,s] == model.A[i,s] * (model.Emissions_unit_cost[s][i]) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_70 = pyo.Constraint(i, model.S, rule = Emissions_unit_cost_value) + + + #This constraint puts upper limit to the emissions bought from the government by each plant s through carbon trading for period i + def CT_emissions_UL (model, i, s): + if CT == 'ON': + return model.CT_emissions[i,s] <= model.plant[s]['UB'] * model.plant[s]['CI'] * model.A[i,s] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_71 = pyo.Constraint(i, model.S, rule = CT_emissions_UL) + + + #This constraint puts lower limit to the emissions bought from the government by each plant s through carbon trading for period i + def CT_emissions_LL (model, i, s): + if CT == 'ON': + return model.CT_emissions[i,s] >= model.plant[s]['LB'] * model.plant[s]['CI'] * model.A[i,s] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_72 = pyo.Constraint(i, model.S, rule = CT_emissions_LL) + + + #This constraint calculates net emissions from a plant s for period i + def CO2_load_plant(model, i, s): + return ((model.net_energy[i,s] * model.plant[s]['CI']) + (model.net_energy_CCS_1[i,s] * model.plant[s]['CI'] * ((1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i]))) + (model.net_energy_CCS_2[i,s] * model.plant[s]['CI'] * ((1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i]))) + (model.solid_1[i,s] * model.SLD_CI['SOLID_1'][i]) + (model.solid_2[i,s] * model.SLD_CI['SOLID_2'][i]) + (model.gas_1[i,s] * model.GAS_CI['GAS_1'][i]) + (model.gas_2[i,s] * model.GAS_CI['GAS_2'][i])) == model.net_plant_emissions[i,s] + + model.Cons_73 = pyo.Constraint(i, model.S, rule = CO2_load_plant) + + + #This constraint calculates the emissions avaiable for trading between plants by each plant s for period i + def CT_emissions_tradable (model, i, s): + if CT == 'ON': + return ((model.ET_plant_new [i,s] + model.CT_emissions[i,s]) - (model.net_plant_emissions[i,s])) == model.ET_plant [i,s] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_74 = pyo.Constraint(i, model.S, rule = CT_emissions_tradable) + + + #This constraint balances the emissions for plant s in period i + def CT_plant_emissions_balance (model, i, s): + if CT == 'ON': + return model.ET_plant [i,s] == model.ET_plant_final [i,s] + model.Emissions_left [i,s] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_75 = pyo.Constraint(i, model.S, rule = CT_plant_emissions_balance) + + + #This constraint calculates balance emissions for all periods (except first one) + def CT_rem_balance_emissions (model, i, s): + if CT == 'ON': + return model.CT_balance_emissions [i] == sum(model.Emissions_left [i,s] for s in model.S) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_76 = pyo.Constraint(i, model.S, rule = CT_rem_balance_emissions) + + + #This constraint calculates balance emissions for all periods (except first one) + def CT_trade_within_plants (model, i, s): + if CT == 'ON': + return sum(model.ET_plant_final[i,s] for s in model.S) == 0 + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_77 = pyo.Constraint(i, model.S, rule = CT_trade_within_plants) + + + #This constraint quantifies the emissions avaible for a plant to be traded within a period + def CT_plant_emissions_value (model, i, s): + if CT == 'ON': + return model.ET_plant_final [i,s] == model.ET_plant_final[i,s] * model.A[i,s] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_78 = pyo.Constraint(i, model.S, rule = CT_plant_emissions_value) + + + #This constraint enables puts an upper limit on emissions trading between plants + def CT_plant_UL_final (model, i, s): + if CT == 'ON': + return model.ET_plant_final [i,s] <= (CT_Percentage_Emissions) * (model.energy[i,s] * (model.plant[s]['CI'])) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_79 = pyo.Constraint(i, model.S, rule = CT_plant_UL_final) + + + #This constraint enables puts a lower limit on emissions trading between plants + def CT_plant_LL_final (model, i, s): + if CT == 'ON': + return model.ET_plant_final [i,s] >= (-1) * (CT_Percentage_Emissions) * (model.energy[i,s] * (model.plant[s]['CI'])) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_80 = pyo.Constraint(i, model.S, rule = CT_plant_LL_final) + + + #This constraint puts limit to balance emissions traded in next period + def CT_balance_emissions_trading (model, i, s): + if CT == 'ON': + if i == 1: + return sum(model.ET_plant_new [i,s] for s in model.S) <= EB + else: + return sum(model.ET_plant_new [i,s] for s in model.S) <= model.CT_balance_emissions[i-1] + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_81 = pyo.Constraint(i, model.S, rule = CT_balance_emissions_trading) + + + #This constraint enables puts an upper limit on emissions between periods + def CT_plant_new_UL (model, i, s): + if CT == 'ON': + return model.ET_plant_new [i,s] <= (model.plant[s]['UB'] * model.plant[s]['CI'] * model.A[i,s]) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_82 = pyo.Constraint(i, model.S, rule = CT_plant_new_UL) + + + #This constraint puts a budget cap on carbon trading + def CT_budget_cap (model, i, s): + if CT == 'ON': + return ((sum((model.CT_emissions[i,s] * model.emissions_unit_cost_new[i,s]) for s in model.S)) + (model.EP['CT Additional Budget'][i])) >= ((-1) * (sum((model.Emissions_left [i,s] * model.emissions_unit_cost_new[i,s]) for s in model.S))) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_83 = pyo.Constraint(i, model.S, rule = CT_budget_cap) + + + #This constraint enables trading within different periods + def CT_periods (model, i, s): + if CT == 'ON': + return sum(model.ET_plant_new[i,s] for s in model.S) + sum(model.ET_plant_final[i,s] for s in model.S) + sum(model.CT_emissions[i,s] for s in model.S) == (sum((model.energy[i,s] * (model.plant[s]['CI'])) for s in model.S)) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_84 = pyo.Constraint(i, model.S, rule = CT_periods) + + + #This constraint calculates the cost paid/earned by each plant s to buy or sell carbon emissions among each other for period i + def CT_cost (model, i, s): + if CT == 'ON': + return model.CT_cost[i,s] == model.emissions_unit_cost_new[i,s] * (model.CT_emissions[i,s] + model.ET_plant_new [i,s] + model.ET_plant_final [i,s] + model.Emissions_left [i,s]) + elif CT == 'OFF': + return pyo.Constraint.Skip + + model.Cons_85 = pyo.Constraint(i, model.S, rule = CT_cost) + + + #This constraint defines the earning of a power plant s by selling stored emissions as a result of implementing CCS for period i + def CO2_selling(model, i, s): + return ((((model.net_energy_CCS[i,s])*(model.plant[s]['CI'])) - ((model.net_energy_CCS_1[i,s]) * (model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i]))) - ((model.net_energy_CCS_2[i,s]) * (model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i])))) * (model.EP['Carbon Emissions Price'][i])) == (model.CCS_selling_price[i,s]) + + model.Cons_86 = pyo.Constraint(i, model.S, rule = CO2_selling) + + + #This constraint limits the stored emissions as a result of CCS for period i + def Stored_emissions_limit(model, i, s): + return model.EP['Carbon Emissions Requirement'][i] >= sum((((model.net_energy_CCS_1[i,s]) * (model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i]))) + ((model.net_energy_CCS_2[i,s])*(model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i])))) for s in model.S) + + model.Cons_87 = pyo.Constraint(i, model.S, rule = Stored_emissions_limit) + + + #Determining the total earning of a power plant s for period i + def Plant_earning(model, i, s): + if 'SOLAR' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Solar)'][i])) + elif 'WIND' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Wind)'][i])) + elif 'HYDRO' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Hydro)'][i])) + elif 'BIOGAS' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Biogas)'][i])) + elif 'BIOMASS' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Biomass)'][i])) + elif 'MSW' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (MSW)'][i])) + elif 'NUCLEAR' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Nuclear)'][i])) + elif 'NG' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (NG)'][i])) + elif 'OIL' in model.plant[s].values(): + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Oil)'][i])) + else: + return model.Plant_earning[i,s] == (model.energy[i,s] * (model.Electricity_price[i,s]) * (1 - model.EP['Tax (Coal)'][i])) + + model.Cons_88 = pyo.Constraint(i, model.S, rule = Plant_earning) + + + #This constraint puts constraint to electricity prices + def Electricity_Prices(model, i, s): + if i == numperiods - 1: + return pyo.Constraint.Skip + else: + return model.Electricity_price[i+1,s] >= (model.Electricity_price[i,s]) * ((1)+(Elec_Price_Inflation)) + + model.Cons_89 = pyo.Constraint(i, model.S, rule = Electricity_Prices) + + + #This constraint defines the initial electricity price for first period + def Electricity_Price_P1(model, i, s): + if i == 1: + return model.Electricity_price[i,s] == (Initial_Elec_Price) + else: + return model.Electricity_price[i,s] <= (Max_Elec_Price) + + model.Cons_90 = pyo.Constraint(i, model.S, rule = Electricity_Price_P1) + + + #The total CO2 load contribution from all energy sources must satisfy most the CO2 emission limit in period i + def total_CO2_load(model, i, s): + return (sum((model.net_energy[i,s] * model.plant[s]['CI']) + (model.net_energy_CCS_1[i,s] * model.plant[s]['CI'] * (1 - model.CCS_data['RR_1'][i]) / (1 - model.CCS_data['X_1'][i])) + (model.net_energy_CCS_2[i,s] * model.plant[s]['CI'] * (1 - model.CCS_data['RR_2'][i]) / (1 - model.CCS_data['X_2'][i])) + + (model.solid_1[i,s] * model.SLD_CI['SOLID_1'][i]) + (model.solid_2[i,s] * model.SLD_CI['SOLID_2'][i]) + + (model.gas_1[i,s] * model.GAS_CI['GAS_1'][i]) + (model.gas_2[i,s] * model.GAS_CI['GAS_2'][i]) for s in model.S) + + (model.EC_NET_1[i] * model.NET_CI['EC_NETs_1'][i]) + + (model.EC_NET_2[i] * model.NET_CI['EC_NETs_2'][i]) + + (model.EC_NET_3[i] * model.NET_CI['EC_NETs_3'][i]) + + (model.EP_NET_1[i] * model.NET_CI['EP_NETs_1'][i]) + + (model.EP_NET_2[i] * model.NET_CI['EP_NETs_2'][i]) + + (model.EP_NET_3[i] * model.NET_CI['EP_NETs_3'][i]) + + (model.REN_SOLAR[i] * model.REN_CI['SOLAR'][i]) + + (model.REN_HYDRO[i] * model.REN_CI['HYDRO'][i]) + + (model.REN_BM[i] * model.REN_CI['BIOMASS'][i]) + + (model.REN_BG[i] * model.REN_CI['BIOGAS'][i]) + + (model.REN_MSW[i] * model.REN_CI['MSW'][i]) == model.new_emission[i]) + + model.Cons_91 = pyo.Constraint(i, model.S, rule = total_CO2_load) + + + #Determining the cumulative total fuel and annualised capital cost for a power plant s for period i + def energy_cost(model, i, s): + if 'SOLAR' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['SOLAR'][i]) + elif 'WIND' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['WIND'][i]) + (AFF * model.CPX_1['WIND'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['WIND'][i]) + elif 'HYDRO' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['HYDRO'][i]) + elif 'BIOGAS' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOGAS'][i]) + elif 'BIOMASS' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOMASS'][i]) + elif 'MSW' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['MSW'][i]) + elif 'NUCLEAR' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['NUCLEAR'][i]) + (AFF * model.CPX_1['NUCLEAR'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['NUCLEAR'][i]) + elif 'NG' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['NG'][i]) + (AFF * model.CPX_1['NG'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['NG'][i]) + elif 'OIL' in model.plant[s].values(): + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['OIL'][i]) + (AFF * model.CPX_1['OIL'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['OIL'][i]) + else: + return model.energy_cost[i,s] == (model.net_energy[i,s] * model.fuel['COAL'][i]) + (AFF * model.CPX_1['COAL'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['COAL'][i]) + + model.Cons_92 = pyo.Constraint(i, model.S, rule = energy_cost) + + + #The summation of cost for each power plant s should equal to the total cost of each period i + def Plant_net_cost(model, i, s): + if CT == 'OFF': + return ((model.Plant_earning[i,s]) - ((model.net_energy_CCS_1[i,s] * model.CCS_data['Cost_CCS_1'][i]) + (model.net_energy_CCS_2[i,s] * model.CCS_data['Cost_CCS_2'][i]) + + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]) + + (model.solid_1[i,s] * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]) + (model.solid_2[i,s] * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]) + + (model.gas_1[i,s] * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]) + (model.gas_2[i,s] * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]) + + (model.energy_cost[i,s]) - (model.CCS_selling_price[i,s]))) == (model.Plant_cost[i,s]) + + elif CT == 'ON': + return (model.Plant_earning[i,s]) - ((model.net_energy_CCS_1[i,s] * model.CCS_data['Cost_CCS_1'][i]) + (model.net_energy_CCS_2[i,s] * model.CCS_data['Cost_CCS_2'][i]) + + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]) + + (model.solid_1[i,s] * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]) + (model.solid_2[i,s] * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]) + + (model.gas_1[i,s] * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]) + (model.gas_2[i,s] * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]) + + (model.energy_cost[i,s]) + (model.CT_cost[i,s]) - (model.CCS_selling_price[i,s])) == (model.Plant_cost[i,s]) + + model.Cons_93 = pyo.Constraint(i, model.S, rule = Plant_net_cost) + + + #Determining the total fuel cost for a power plant s for period i + def fuel_cost(model, i, s): + if 'SOLAR' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['SOLAR'][i]) + elif 'WIND' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['WIND'][i]) + elif 'HYDRO' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['HYDRO'][i]) + elif 'BIOGAS' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOGAS'][i]) + elif 'BIOMASS' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['BIOMASS'][i]) + elif 'MSW' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['MSW'][i]) + elif 'NUCLEAR' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['NUCLEAR'][i]) + elif 'NG' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['NG'][i]) + elif 'OIL' in model.plant[s].values(): + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['OIL'][i]) + else: + return model.fuel_cost[i,s] == (model.net_energy[i,s] * model.fuel['COAL'][i]) + + model.Cons_94 = pyo.Constraint(i, model.S, rule = fuel_cost) + + + #Determining the total annualised capital cost for a power plant s for period i + def annualized_capital_cost(model, i, s): + if 'SOLAR' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['SOLAR'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['SOLAR'][i]) + elif 'WIND' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['WIND'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['WIND'][i]) + elif 'HYDRO' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['HYDRO'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['HYDRO'][i]) + elif 'BIOGAS' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['BIOGAS'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOGAS'][i]) + elif 'BIOMASS' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['BIOMASS'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['BIOMASS'][i]) + elif 'MSW' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['MSW'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['MSW'][i]) + elif 'NUCLEAR' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['NUCLEAR'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['NUCLEAR'][i]) + elif 'NG' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['NG'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['NG'][i]) + elif 'OIL' in model.plant[s].values(): + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['OIL'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['OIL'][i]) + else: + return model.annualized_capital_cost[i,s] == (AFF * model.CPX_1['COAL'][i] * model.energy[i,s]) + (AFF * model.energy[i,s] * model.CPX_2['COAL'][i]) + + model.Cons_95 = pyo.Constraint(i, model.S, rule = annualized_capital_cost) + + + #The summation of cost for each power plant s should equal to the total cost of each period i + def sum_cost(model, i, s): + if CT == 'OFF': + return (sum((model.net_energy_CCS_1[i,s] * model.CCS_data['Cost_CCS_1'][i]) + (model.net_energy_CCS_2[i,s] * model.CCS_data['Cost_CCS_2'][i]) + + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]) + + (model.solid_1[i,s] * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]) + (model.solid_2[i,s] * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]) + + (model.gas_1[i,s] * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]) + (model.gas_2[i,s] * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]) for s in model.S) + + (model.EC_NET_1[i] * model.NET_COST['EC_NETs_1'][i]) + (AFF * model.CPX_1['EC_NETs_1'][i] * model.G[i]) + (AFF * model.EC_NET_1[i] * model.CPX_2['EC_NETs_1'][i]) + + (model.EC_NET_2[i] * model.NET_COST['EC_NETs_2'][i]) + (AFF * model.CPX_1['EC_NETs_2'][i] * model.H[i]) + (AFF * model.EC_NET_2[i] * model.CPX_2['EC_NETs_2'][i]) + + (model.EC_NET_3[i] * model.NET_COST['EC_NETs_3'][i]) + (AFF * model.CPX_1['EC_NETs_3'][i] * model.I[i]) + (AFF * model.EC_NET_3[i] * model.CPX_2['EC_NETs_3'][i]) + + (model.EP_NET_1[i] * model.NET_COST['EP_NETs_1'][i]) + (AFF * model.CPX_1['EP_NETs_1'][i] * model.D[i]) + (AFF * model.EP_NET_1[i] * model.CPX_2['EP_NETs_1'][i]) + + (model.EP_NET_2[i] * model.NET_COST['EP_NETs_2'][i]) + (AFF * model.CPX_1['EP_NETs_2'][i] * model.E[i]) + (AFF * model.EP_NET_2[i] * model.CPX_2['EP_NETs_2'][i]) + + (model.EP_NET_3[i] * model.NET_COST['EP_NETs_3'][i]) + (AFF * model.CPX_1['EP_NETs_3'][i] * model.F[i]) + (AFF * model.EP_NET_3[i] * model.CPX_2['EP_NETs_3'][i]) + + (model.REN_SOLAR[i] * model.REN_COST['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.J[i]) + (AFF * model.REN_SOLAR[i] * model.CPX_2['SOLAR'][i]) + + (model.REN_HYDRO[i] * model.REN_COST['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.K[i]) + (AFF * model.REN_HYDRO[i] * model.CPX_2['HYDRO'][i]) + + (model.REN_BM[i] * model.REN_COST['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.L[i]) + (AFF * model.REN_BM[i] * model.CPX_2['BIOMASS'][i]) + + (model.REN_BG[i] * model.REN_COST['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.M[i]) + (AFF * model.REN_BG[i] * model.CPX_2['BIOGAS'][i]) + + (model.REN_MSW[i] * model.REN_COST['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.N[i]) + (AFF * model.REN_MSW[i] * model.CPX_2['MSW'][i]) + + sum(model.energy_cost[i,s] for s in model.S) - sum(model.CCS_selling_price[i,s] for s in model.S) == model.sum_cost[i]) + + elif CT == 'ON': + return (sum((model.net_energy_CCS_1[i,s] * model.CCS_data['Cost_CCS_1'][i]) + (model.net_energy_CCS_2[i,s] * model.CCS_data['Cost_CCS_2'][i]) + + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]) + + (model.solid_1[i,s] * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]) + (model.solid_2[i,s] * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]) + + (model.gas_1[i,s] * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]) + (model.gas_2[i,s] * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]) for s in model.S) + + (model.EC_NET_1[i] * model.NET_COST['EC_NETs_1'][i]) + (AFF * model.CPX_1['EC_NETs_1'][i] * model.G[i]) + (AFF * model.EC_NET_1[i] * model.CPX_2['EC_NETs_1'][i]) + + (model.EC_NET_2[i] * model.NET_COST['EC_NETs_2'][i]) + (AFF * model.CPX_1['EC_NETs_2'][i] * model.H[i]) + (AFF * model.EC_NET_2[i] * model.CPX_2['EC_NETs_2'][i]) + + (model.EC_NET_3[i] * model.NET_COST['EC_NETs_3'][i]) + (AFF * model.CPX_1['EC_NETs_3'][i] * model.I[i]) + (AFF * model.EC_NET_3[i] * model.CPX_2['EC_NETs_3'][i]) + + (model.EP_NET_1[i] * model.NET_COST['EP_NETs_1'][i]) + (AFF * model.CPX_1['EP_NETs_1'][i] * model.D[i]) + (AFF * model.EP_NET_1[i] * model.CPX_2['EP_NETs_1'][i]) + + (model.EP_NET_2[i] * model.NET_COST['EP_NETs_2'][i]) + (AFF * model.CPX_1['EP_NETs_2'][i] * model.E[i]) + (AFF * model.EP_NET_2[i] * model.CPX_2['EP_NETs_2'][i]) + + (model.EP_NET_3[i] * model.NET_COST['EP_NETs_3'][i]) + (AFF * model.CPX_1['EP_NETs_3'][i] * model.F[i]) + (AFF * model.EP_NET_3[i] * model.CPX_2['EP_NETs_3'][i]) + + (model.REN_SOLAR[i] * model.REN_COST['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.J[i]) + (AFF * model.REN_SOLAR[i] * model.CPX_2['SOLAR'][i]) + + (model.REN_HYDRO[i] * model.REN_COST['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.K[i]) + (AFF * model.REN_HYDRO[i] * model.CPX_2['HYDRO'][i]) + + (model.REN_BM[i] * model.REN_COST['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.L[i]) + (AFF * model.REN_BM[i] * model.CPX_2['BIOMASS'][i]) + + (model.REN_BG[i] * model.REN_COST['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.M[i]) + (AFF * model.REN_BG[i] * model.CPX_2['BIOGAS'][i]) + + (model.REN_MSW[i] * model.REN_COST['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.N[i]) + (AFF * model.REN_MSW[i] * model.CPX_2['MSW'][i]) + + sum(model.energy_cost[i,s] for s in model.S) + sum(model.CT_cost[i,s] for s in model.S) - sum(model.CCS_selling_price[i,s] for s in model.S) == model.sum_cost[i]) + + model.Cons_96 = pyo.Constraint(i, model.S, rule = sum_cost) + + + def Total_profit (model, i, s): + return (sum(model.Plant_earning[i,s] for s in model.S)) - (model.sum_cost[i]) == model.total_profit[i] + + model.Cons_97 = pyo.Constraint(i, model.S, rule = Total_profit) + + + #This constraint ensures the net profitability over period i + def Plant_profit_net (model, i, s): + if Plant_Profit == 'ON': + return model.Plant_cost[i,s] >= 0 + elif Plant_Profit == 'OFF': + return pyo.Constraint.Skip + + model.Cons_98 = pyo.Constraint(i, model.S, rule = Plant_profit_net) + + + #For the minimum budget objective function, the total cost is minimised, subject to the satisfaction of the CO2 emission limit + #For the minimum emission objective function, the total emission is minimised, subject to the available budgetary constraint + def objective_constraint(model, i): + if flag == 'min_budget': + return model.new_emission[i] <= model.EP['Limit'][i] + elif flag == 'min_emission': + return model.sum_cost[i] <= model.EP['Budget'][i] + else: + return pyo.Constraint.Skip + + model.Cons_99 = pyo.Constraint(i, rule = objective_constraint) + + #Constraints 100 and 101 are to check the feasibility of the model, and it also performs maximization of profits + def objective_constraint(model, i): + if flag == 'max_profit': + return model.new_emission[i] <= model.EP['Limit'][i] + else: + return pyo.Constraint.Skip + + model.Cons_100 = pyo.Constraint(i, rule = objective_constraint) + + + def objective_constraint(model, i): + if flag == 'max_profit': + return model.sum_cost[i] <= model.EP['Budget'][i] + else: + return pyo.Constraint.Skip + + model.Cons_101 = pyo.Constraint(i, rule = objective_constraint) + + + #opt = SolverFactory('octeract-engine', tee = True) + #results = opt.solve(model) + + opt = SolverFactory('gams') + #sys.exit() + + results = opt.solve(model, solver = 'scip', tee = True, add_options = ['option reslim = 36000;', 'option optcr = 0.01;', 'option optca = 0.01;', 'GAMS_MODEL.nodlim = 1000000;']) + #optca: Absolute optimality criterion for MIPs, and is of the order of 10^-9 by default + #optcr: This option specifies a relative termination tolerance for use in solving MIP problems, default is 5E-8, set it between 0.01 and 0.05 for faster computation, having a low value for this gives better results + + #opt = SolverFactory('gurobi') + #results = opt.solve(model, tee = True) + + print(results) + #model.pprint() + return model + +def multiperiod_energy_planning_results(model, i): + energy_planning = pd.DataFrame() + + for s in model.plant.keys(): + energy_planning.loc[s, 'Fuel'] = model.plant[s]['Fuel'] + energy_planning.loc[s, 'Energy Generation'] = model.A[i,s]() + energy_planning.loc[s, 'Gross Energy (TWh/y)'] = round(model.energy[i,s](), 3) + energy_planning.loc[s, 'CO2 Intensity (Mt/TWh)'] = model.plant[s]['CI'] + energy_planning.loc[s, 'CCS_1 CI'] = round(model.CI_RET_1[i,s](), 3) + energy_planning.loc[s, 'CCS_2 CI'] = round(model.CI_RET_2[i,s](), 3) + energy_planning.loc[s, 'CCS_1 Selection'] = model.B[i,s]() + energy_planning.loc[s, 'CCS_1 Ret (TWh/y)'] = round(model.CCS_1[i,s](), 3) + energy_planning.loc[s, 'CCS_2 Selection'] = model.C[i,s]() + energy_planning.loc[s, 'CCS_2 Ret (TWh/y)'] = round(model.CCS_2[i,s](), 3) + energy_planning.loc[s, 'Net Energy wo CCS'] = round(model.net_energy[i,s](), 3) + energy_planning.loc[s, 'Net Energy w CCS_1'] = round(model.net_energy_CCS_1[i,s](), 3) + energy_planning.loc[s, 'Net Energy w CCS_2'] = round(model.net_energy_CCS_2[i,s](), 3) + energy_planning.loc[s, 'Solid_1 Selection'] = model.O[i,s]() + energy_planning.loc[s, 'SOLID_1 (TWh/y)'] = round(model.solid_1[i,s](), 3) + energy_planning.loc[s, 'Solid_2 Selection'] = model.P[i,s]() + energy_planning.loc[s, 'SOLID_2 (TWh/y)'] = round(model.solid_2[i,s](), 3) + energy_planning.loc[s, 'Gas_1 Selection'] = model.Q[i,s]() + energy_planning.loc[s, 'GAS_1 (TWh/y)'] = round(model.gas_1[i,s](), 3) + energy_planning.loc[s, 'Gas_2 Selection'] = model.R[i,s]() + energy_planning.loc[s, 'GAS_2 (TWh/y)'] = round(model.gas_2[i,s](), 3) + energy_planning.loc[s, 'Net Energy (TWh/y)'] = round(model.net_energy[i,s]() + model.net_energy_CCS_1[i,s]() + model.net_energy_CCS_2[i,s]() + model.solid_1[i,s]() + model.solid_2[i,s]() + model.gas_1[i,s]() + model.gas_2[i,s](), 3) + energy_planning.loc[s, 'CO2 Load (Mt/y)'] = round(model.net_plant_emissions[i,s](), 3) + #Below lines are commented by the if/else statement if CT is turned off + if CT == 'ON': + energy_planning.loc[s, 'CT Emissions Price (mil USD / Mt)'] = round(model.emissions_unit_cost_new[i,s](), 3) + energy_planning.loc[s, 'Emissions bought from previous period (Mt/y)'] = round(model.ET_plant_new[i,s](), 3) + energy_planning.loc[s, 'Emission Rights Bought by Each Plant from government (Mt/y)'] = round(model.CT_emissions [i,s](), 3) + energy_planning.loc[s, 'Emissions rights available for trading within companies (Mt/y)'] = round(model.ET_plant[i,s](), 3) + energy_planning.loc[s, 'Emissions traded within a company (Mt/y)'] = round(model.ET_plant_final[i,s](), 3) + energy_planning.loc[s, 'Net Emissions traded within a company (Mt/y)'] = round(((model.ET_plant[i,s]()) - (model.ET_plant_final[i,s]())), 3) + energy_planning.loc[s, 'CT Cost Paid to government to buy emissions from previous period (mil USD / y)'] = round((model.ET_plant_new[i,s]() * model.emissions_unit_cost_new[i,s]()), 3) + energy_planning.loc[s, 'CT Cost Paid by Plant to government (mil USD / y)'] = round((model.CT_emissions [i,s]() * model.emissions_unit_cost_new[i,s]()), 3) + energy_planning.loc[s, 'CT Cost Paid/earned by each plant as a result of trading with each other (mil USD / y)'] = round(model.emissions_unit_cost_new[i,s]() * model.ET_plant_final [i,s](), 3) + energy_planning.loc[s, 'Balance Emissions to be traded in next period'] = round(model.Emissions_left[i,s](), 3) + energy_planning.loc[s, 'Price paid/earned for balance emissions (mil USD / y)'] = round((model.Emissions_left[i,s]() * model.emissions_unit_cost_new[i,s]()), 3) + else: + pass + #Above lines are commented by the if/else statement if CT is turned off + energy_planning.loc[s, 'Cumulative total fuel and annualized capital cost (mil USD/y)'] = round(model.energy_cost[i,s](), 3) + energy_planning.loc[s, 'Annualized capital cost (mil USD/y)'] = round(model.annualized_capital_cost[i,s](), 3) + energy_planning.loc[s, 'Fuel cost (mil USD/y)'] = round(model.fuel_cost[i,s](), 3) + energy_planning.loc[s, 'Cost contribution of CCS_1 (mil USD/y)'] = round((model.net_energy_CCS_1[i,s]() * model.CCS_data['Cost_CCS_1'][i]) + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]()), 3) + energy_planning.loc[s, 'Cost contribution of CCS_2 (mil USD/y)'] = round((model.net_energy_CCS_2[i,s]() * model.CCS_data['Cost_CCS_2'][i]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]()), 3) + energy_planning.loc[s, 'Cost contribution of Solid_1 (mil USD/y)'] = round((model.solid_1[i,s]() * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]()), 3) + energy_planning.loc[s, 'Cost contribution of Solid_2 (mil USD/y)'] = round((model.solid_2[i,s]() * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]()), 3) + energy_planning.loc[s, 'Cost contribution of Gas_1 (mil USD/y)'] = round((model.gas_1[i,s]() * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]()), 3) + energy_planning.loc[s, 'Cost contribution of Gas_2 (mil USD/y)'] = round((model.gas_2[i,s]() * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]()), 3) + energy_planning.loc[s, 'CO2 Sold by Plant (Mt/y)'] = round((((model.net_energy_CCS[i,s]())*(model.plant[s]['CI'])) - ((model.net_energy_CCS_1[i,s]())*(model.CI_RET_1[i,s]())) - ((model.net_energy_CCS_2[i,s]())*(model.CI_RET_2[i,s]()))), 3) + energy_planning.loc[s, 'Earning by CO2 selling (mil USD / y)'] = round(model.CCS_selling_price[i,s](), 3) + energy_planning.loc[s, 'Electricty Prices (mil USD / TWh)'] = round(model.Electricity_price[i,s]() * model.A[i,s](), 3) + energy_planning.loc[s, 'Earning by selling electricty (mil USD / y)'] = round(model.Plant_earning[i,s](), 3) + energy_planning.loc[s, 'Plant Profit (mil USD/y)'] = round(model.Plant_cost[i,s](), 3) + energy_planning.loc[s, 'Total Cost (mil USD/y)'] = round((model.Plant_earning[i,s]()) - (model.Plant_cost[i,s]()), 3) + + energy_planning.loc['EP_NET_1', 'Fuel'] = 'EP_NET_1' + energy_planning.loc['EP_NET_2', 'Fuel'] = 'EP_NET_2' + energy_planning.loc['EP_NET_3', 'Fuel'] = 'EP_NET_3' + energy_planning.loc['EC_NET_1', 'Fuel'] = 'EC_NET_1' + energy_planning.loc['EC_NET_2', 'Fuel'] = 'EC_NET_2' + energy_planning.loc['EC_NET_3', 'Fuel'] = 'EC_NET_3' + energy_planning.loc['EP_NET_1', 'Energy Generation'] = model.D[i]() + energy_planning.loc['EP_NET_2', 'Energy Generation'] = model.E[i]() + energy_planning.loc['EP_NET_3', 'Energy Generation'] = model.F[i]() + energy_planning.loc['EC_NET_1', 'Energy Generation'] = model.G[i]() + energy_planning.loc['EC_NET_2', 'Energy Generation'] = model.H[i]() + energy_planning.loc['EC_NET_3', 'Energy Generation'] = model.I[i]() + energy_planning.loc['EP_NET_1', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_1'][i] + energy_planning.loc['EP_NET_2', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_2'][i] + energy_planning.loc['EP_NET_3', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EP_NETs_3'][i] + energy_planning.loc['EC_NET_1', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_1'][i] + energy_planning.loc['EC_NET_2', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_2'][i] + energy_planning.loc['EC_NET_3', 'CO2 Intensity (Mt/TWh)'] = model.NET_CI['EC_NETs_3'][i] + energy_planning.loc['EP_NET_1', 'Net Energy (TWh/y)'] = round(model.EP_NET_1[i](), 3) + energy_planning.loc['EP_NET_2', 'Net Energy (TWh/y)'] = round(model.EP_NET_2[i](), 3) + energy_planning.loc['EP_NET_3', 'Net Energy (TWh/y)'] = round(model.EP_NET_3[i](), 3) + energy_planning.loc['EC_NET_1', 'Net Energy (TWh/y)'] = round(model.EC_NET_1[i](), 3) + energy_planning.loc['EC_NET_2', 'Net Energy (TWh/y)'] = round(model.EC_NET_2[i](), 3) + energy_planning.loc['EC_NET_3', 'Net Energy (TWh/y)'] = round(model.EC_NET_3[i](), 3) + energy_planning.loc['EP_NET_1', 'CO2 Load (Mt/y)'] = round(model.EP_NET_1[i]() * model.NET_CI['EP_NETs_1'][i], 3) + energy_planning.loc['EP_NET_2', 'CO2 Load (Mt/y)'] = round(model.EP_NET_2[i]() * model.NET_CI['EP_NETs_2'][i], 3) + energy_planning.loc['EP_NET_3', 'CO2 Load (Mt/y)'] = round(model.EP_NET_3[i]() * model.NET_CI['EP_NETs_3'][i], 3) + energy_planning.loc['EC_NET_1', 'CO2 Load (Mt/y)'] = round(model.EC_NET_1[i]() * model.NET_CI['EC_NETs_1'][i], 3) + energy_planning.loc['EC_NET_2', 'CO2 Load (Mt/y)'] = round(model.EC_NET_2[i]() * model.NET_CI['EC_NETs_2'][i], 3) + energy_planning.loc['EC_NET_3', 'CO2 Load (Mt/y)'] = round(model.EC_NET_3[i]() * model.NET_CI['EC_NETs_3'][i], 3) + energy_planning.loc['SOLAR', 'Fuel'] = 'SOLAR' + energy_planning.loc['HYDRO', 'Fuel'] = 'HYDRO' + energy_planning.loc['BIOMASS', 'Fuel'] = 'BIOMASS' + energy_planning.loc['BIOGAS', 'Fuel'] = 'BIOGAS' + energy_planning.loc['MSW', 'Fuel'] = 'MSW' + energy_planning.loc['SOLAR', 'Energy Generation'] = model.J[i]() + energy_planning.loc['HYDRO', 'Energy Generation'] = model.K[i]() + energy_planning.loc['BIOMASS', 'Energy Generation'] = model.L[i]() + energy_planning.loc['BIOGAS', 'Energy Generation'] = model.M[i]() + energy_planning.loc['MSW', 'Energy Generation'] = model.N[i]() + energy_planning.loc['SOLAR', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['SOLAR'][i] + energy_planning.loc['HYDRO', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['HYDRO'][i] + energy_planning.loc['BIOMASS', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['BIOMASS'][i] + energy_planning.loc['BIOGAS', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['BIOGAS'][i] + energy_planning.loc['MSW', 'CO2 Intensity (Mt/TWh)'] = model.REN_CI['MSW'][i] + energy_planning.loc['SOLAR', 'Net Energy (TWh/y)'] = round(model.REN_SOLAR[i](), 3) + energy_planning.loc['SOLAR', 'CO2 Load (Mt/y)'] = round(model.REN_SOLAR[i]() * model.REN_CI['SOLAR'][i], 3) + energy_planning.loc['HYDRO', 'Net Energy (TWh/y)'] = round(model.REN_HYDRO[i](), 3) + energy_planning.loc['HYDRO', 'CO2 Load (Mt/y)'] = round(model.REN_HYDRO[i]() * model.REN_CI['HYDRO'][i], 3) + energy_planning.loc['BIOMASS', 'Net Energy (TWh/y)'] = round(model.REN_BM[i](), 3) + energy_planning.loc['BIOMASS', 'CO2 Load (Mt/y)'] = round(model.REN_BM[i]() * model.REN_CI['BIOMASS'][i], 3) + energy_planning.loc['BIOGAS', 'Net Energy (TWh/y)'] = round(model.REN_BG[i](), 3) + energy_planning.loc['BIOGAS', 'CO2 Load (Mt/y)'] = round(model.REN_BG[i]() * model.REN_CI['BIOGAS'][i], 3) + energy_planning.loc['MSW', 'Net Energy (TWh/y)'] = round(model.REN_MSW[i](), 3) + energy_planning.loc['MSW', 'CO2 Load (Mt/y)'] = round(model.REN_MSW[i]() * model.REN_CI['MSW'][i], 3) + energy_planning.loc['TOTAL', 'CO2 Load (Mt/y)'] = round(model.new_emission[i](), 3) + energy_planning.loc['EC_NET_1', 'Total Cost (mil USD/y)'] = round(sum(((model.EC_NET_1[i]() * model.NET_COST['EC_NETs_1'][i]) + (AFF * model.CPX_1['EC_NETs_1'][i] * model.G[i]()) + (AFF * model.EC_NET_1[i]() * model.CPX_2['EC_NETs_1'][i])) for s in model.S), 3) + energy_planning.loc['EC_NET_2', 'Total Cost (mil USD/y)'] = round(sum(((model.EC_NET_2[i]() * model.NET_COST['EC_NETs_2'][i]) + (AFF * model.CPX_1['EC_NETs_2'][i] * model.H[i]()) + (AFF * model.EC_NET_2[i]() * model.CPX_2['EC_NETs_2'][i])) for s in model.S), 3) + energy_planning.loc['EC_NET_3', 'Total Cost (mil USD/y)'] = round(sum(((model.EC_NET_3[i]() * model.NET_COST['EC_NETs_3'][i]) + (AFF * model.CPX_1['EC_NETs_3'][i] * model.I[i]()) + (AFF * model.EC_NET_3[i]() * model.CPX_2['EC_NETs_3'][i])) for s in model.S), 3) + energy_planning.loc['EP_NET_1', 'Total Cost (mil USD/y)'] = round(sum(((model.EP_NET_1[i]() * model.NET_COST['EP_NETs_1'][i]) + (AFF * model.CPX_1['EP_NETs_1'][i] * model.D[i]()) + (AFF * model.EP_NET_1[i]() * model.CPX_2['EP_NETs_1'][i])) for s in model.S), 3) + energy_planning.loc['EP_NET_2', 'Total Cost (mil USD/y)'] = round(sum(((model.EP_NET_2[i]() * model.NET_COST['EP_NETs_2'][i]) + (AFF * model.CPX_1['EP_NETs_2'][i] * model.E[i]()) + (AFF * model.EP_NET_2[i]() * model.CPX_2['EP_NETs_2'][i])) for s in model.S), 3) + energy_planning.loc['EP_NET_3', 'Total Cost (mil USD/y)'] = round(sum(((model.EP_NET_3[i]() * model.NET_COST['EP_NETs_3'][i]) + (AFF * model.CPX_1['EP_NETs_3'][i] * model.F[i]()) + (AFF * model.EP_NET_3[i]() * model.CPX_2['EP_NETs_3'][i])) for s in model.S), 3) + energy_planning.loc['SOLAR', 'Total Cost (mil USD/y)'] = round(sum(((model.REN_SOLAR[i]() * model.REN_COST['SOLAR'][i]) + (AFF * model.CPX_1['SOLAR'][i] * model.J[i]()) + (AFF * model.REN_SOLAR[i]() * model.CPX_2['SOLAR'][i])) for s in model.S), 3) + energy_planning.loc['HYDRO', 'Total Cost (mil USD/y)'] = round(sum(((model.REN_HYDRO[i]() * model.REN_COST['HYDRO'][i]) + (AFF * model.CPX_1['HYDRO'][i] * model.K[i]()) + (AFF * model.REN_HYDRO[i]() * model.CPX_2['HYDRO'][i])) for s in model.S), 3) + energy_planning.loc['BIOMASS', 'Total Cost (mil USD/y)'] = round(sum(((model.REN_BM[i]() * model.REN_COST['BIOMASS'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.L[i]()) + (AFF * model.REN_BM[i]() * model.CPX_2['BIOMASS'][i])) for s in model.S), 3) + energy_planning.loc['BIOGAS', 'Total Cost (mil USD/y)'] = round(sum(((model.REN_BG[i]() * model.REN_COST['BIOGAS'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.M[i]()) + (AFF * model.REN_BG[i]() * model.CPX_2['BIOGAS'][i])) for s in model.S), 3) + energy_planning.loc['MSW', 'Total Cost (mil USD/y)'] = round(sum(((model.REN_MSW[i]() * model.REN_COST['MSW'][i]) + (AFF * model.CPX_1['MSW'][i] * model.N[i]()) + (AFF * model.REN_MSW[i]() * model.CPX_2['MSW'][i])) for s in model.S), 3) + #Below lines are commented by the if/else statement if CT is turned off + if CT == 'ON': + energy_planning.loc['TOTAL', 'Emissions bought from previous period (Mt/y)'] = round(sum(model.ET_plant_new [i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Emission Rights Bought by Each Plant from government (Mt/y)'] = round(sum(model.CT_emissions [i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Emissions rights available for trading within companies (Mt/y)'] = round(sum(model.ET_plant[i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Emissions traded within a company (Mt/y)'] = round(sum(model.ET_plant_final [i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'CT Cost Paid to government to buy emissions from previous period (mil USD / y)'] = round(sum((model.ET_plant_new[i,s]() * model.emissions_unit_cost_new[i,s]())for s in model.S) , 3) + energy_planning.loc['TOTAL', 'CT Cost Paid by Plant to government (mil USD / y)'] = round(sum((model.CT_emissions [i,s]() * model.emissions_unit_cost_new[i,s]()) for s in model.S), 3) + energy_planning.loc['TOTAL', 'CT Cost Paid/earned by each plant as a result of trading with each other (mil USD / y)'] = round(sum((model.emissions_unit_cost_new[i,s]() * model.ET_plant_final [i,s]()) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Balance Emissions to be traded in next period'] = round(model.CT_balance_emissions[i](), 3) + energy_planning.loc['TOTAL', 'Price paid/earned for balance emissions (mil USD / y)'] = round(sum((model.Emissions_left[i,s]() * model.emissions_unit_cost_new[i,s]()) for s in model.S) , 3) + else: + pass + #Above lines are commented by the if/else statement if CT is turned off + energy_planning.loc['TOTAL', 'Cumulative total fuel and annualized capital cost (mil USD/y)'] = round(sum(model.energy_cost[i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Annualized capital cost (mil USD/y)'] = round(sum(model.annualized_capital_cost[i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Fuel cost (mil USD/y)'] = round(sum(model.fuel_cost[i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Cost contribution of CCS_1 (mil USD/y)'] = round(sum(((model.net_energy_CCS_1[i,s]() * model.CCS_data['Cost_CCS_1'][i]) + (AFF * model.CCS_data['FX_Cost_CCS_1'][i] * model.B[i,s]())) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Cost contribution of CCS_2 (mil USD/y)'] = round(sum(((model.net_energy_CCS_2[i,s]() * model.CCS_data['Cost_CCS_2'][i]) + (AFF * model.CCS_data['FX_Cost_CCS_2'][i] * model.C[i,s]())) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Cost contribution of Solid_1 (mil USD/y)'] = round(sum(((model.solid_1[i,s]() * model.SLD_COST['SOLID_1'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.O[i,s]())) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Cost contribution of Solid_2 (mil USD/y)'] = round(sum(((model.solid_2[i,s]() * model.SLD_COST['SOLID_2'][i]) + (AFF * model.CPX_1['BIOMASS'][i] * model.P[i,s]())) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Cost contribution of Gas_1 (mil USD/y)'] = round(sum(((model.gas_1[i,s]() * model.GAS_COST['GAS_1'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.Q[i,s]())) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Cost contribution of Gas_2 (mil USD/y)'] = round(sum(((model.gas_2[i,s]() * model.GAS_COST['GAS_2'][i]) + (AFF * model.CPX_1['BIOGAS'][i] * model.R[i,s]())) for s in model.S), 3) + energy_planning.loc['TOTAL', 'Earning by CO2 selling (mil USD / y)'] = round(sum(model.CCS_selling_price[i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Earning by selling electricty (mil USD / y)'] = round(sum(model.Plant_earning[i,s]() for s in model.S), 3) + energy_planning.loc['TOTAL', 'Plant Profit (mil USD/y)'] = round(model.total_profit[i](), 3) + energy_planning.loc['TOTAL', 'Total Cost (mil USD/y)'] = round(model.sum_cost[i](), 3) + + writer = pd.ExcelWriter(file_name, engine = 'openpyxl', mode = 'a', if_sheet_exists = 'new') + #writer = pd.ExcelWriter(file_name, mode = 'a', if_sheet_exists = 'new') + energy_planning.to_excel(writer, sheet_name = 'Results_Period_1') + writer.save() + + return + + +model = multiperiod_energy_planning(model, periods) + +emissions_each_period = [] +for i in periods: + emissions_each_period.append(model.new_emission[i].value) + +cost_each_period = [] +for i in periods: + cost_each_period.append(model.sum_cost[i].value) + +profit_each_period = [] +for i in periods: + profit_each_period.append(((sum((model.Plant_earning[i,s].value) for s in model.S)) - (model.sum_cost[i].value))) + + +wb = load_workbook(file_name) +sheet_results = wb['FINAL_RESULTS'] + +df = pd.DataFrame({'Emissions': emissions_each_period}) + +for index, row in df.iterrows(): + cell = 'B%d' % (index + 2) + sheet_results[cell] = row[0] + +wb.save(file_name) + + +df_0 = pd.DataFrame({'Cost': cost_each_period}) + +for index0, row0 in df_0.iterrows(): + cell0 = 'C%d' % (index0 + 2) + sheet_results[cell0] = row0[0] + +wb.save(file_name) + +df_1 = pd.DataFrame({'Profit': profit_each_period}) + +for index1, row1 in df_1.iterrows(): + cell1 = 'D%d' % (index1 + 2) + sheet_results[cell1] = row1[0] + +wb.save(file_name) + +#Below lines print/display the results in excel-based user interface +if Print_Result == 'YES': + for i in list(range(1, numperiods,1)): + multiperiod_energy_planning_results(model,i) +else: + pass diff --git a/Indirect Optimization Model/IDO_EP_Optimal_Decarbonisation_Run_File.py b/Indirect Optimization Model/IDO_EP_Optimal_Decarbonisation_Run_File.py new file mode 100644 index 0000000..23af5a6 --- /dev/null +++ b/Indirect Optimization Model/IDO_EP_Optimal_Decarbonisation_Run_File.py @@ -0,0 +1,20 @@ +''' +Created on 24th September 2021 +Last Modified in March 2023 + +This script presents the mathematical optimisation formulation for an energy planning scenario in a specific geographical region or district. +Renewable energy and fossil-based sources make up the power generation in a specific geographical region or district. +Each period consists of its respective energy demand and CO2 emission limit. +EP-NETs, EC-NETs, CCS/CCSU and Carbon Trading may be utilised to achieve the CO2 emission limit for each period. +This energy planning mathematical formulation may be optimised subject to either a mininum cost or minimum emission objective function. +There is an addtional formulation to optimize for maximum profit which satisfies both emission targets and budget contraints, which proves the feasibility of data/model, and provides effective initial values. + +@author: Gul, Purusothmn, Dr Michael Short, Prof Dr Dominic C.Y.F. +''' +import os + +path = os.chdir(r'C:\Users\pc\Desktop\DECO2 v2023\Indirect Optimization Model') + +cwd = os.getcwd() + +import IDO_EP_Optimal_Decarbonisation_Model_Python diff --git a/Indirect Optimization Model/LICENSE b/Indirect Optimization Model/LICENSE new file mode 100644 index 0000000..0ad25db --- /dev/null +++ b/Indirect Optimization Model/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, 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Please read the user manual in the main folder for information on the formulation and how the inputs work. For details on the partnership, please visit our site: + +https://www.surrey.ac.uk/research-projects/software-framework-optimal-decarbonisation-planning-asean-countries diff --git a/Indirect Optimization Model/__pycache__/IDO_Base_Model_Python.cpython-38.pyc b/Indirect Optimization Model/__pycache__/IDO_Base_Model_Python.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7df2ee5cda9781f8e6559a1161b2f142a52685f2 GIT binary patch literal 58687 zcmeHw3w&Hhb@#rslGd^;+p=scj3( z-MjKCazgt1A^q;`+;h&HnKO?wXP)=K?AeJB{(kb=za4t#-Qm#R(Tn+4ig7n25Z(p=jPzINyG=|TD3>#S#t%2tP}Mj@kq_N`R7AcQ@UAVW&u+#8Xk#B_yP%^ z4S1s`M2p1C0j9|au~5S20zS`xFOu;2KKNn@Zw7pULEkFjEk5{V621`dMF#y63194k zUoPRTKKN1zzsv`}Lc*5-ZpxLE@XLMhD}!K>8T(ejx14;UabaliTD+}b_-gQJ2A?(78gUIn z)>_wD>qMh32JywswW5?i5(P8b7Ja$<-C68Uq z;}#!}_15*wW4E>2;Bm{~tzv_9i{#O3-70zXGLJ_M9-jP%6ivvP4&?cUGZEx!^q#MY zNPnnpW9G@4P$HxhHcnnOc`hC3-W3VscMjWQVgrXLHG0GP#M2 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z+7x$?PHYsa&Z)68xe?30iUQL9T&@Jp0!x1x-O3CdkzYM?n_WCJ4t*-@D=7(0LnsBD z4l2=bbDk#;E}974?48+en$<4zrzvd_G3?Y)>$mgAacT^oFR@NIQM@=YwEj{1<3tn_ z!|8QSoRRCC7_jS5I6&hCD4Z5zR6C>ET~xbU-MkggsoM(0KQU_E1h%2V6L4ZzH-sB- za Date: Sun, 2 Apr 2023 00:56:53 +0500 Subject: [PATCH 14/24] Create README.md --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..c1733c0 --- /dev/null +++ b/README.md @@ -0,0 +1,6 @@ +This version adds carbon trading into previously published DECO2. + +Direct optimisation model involves incorporating various types of emissions and prices related to carbon trading as variables, leading to the creation of +a MINLP system consisting of equations and constraints. + +Indirect optimization model takes emission rights prices as parameters, leading to less non-linearity when compared to direct optimization model. From 90f36a290acc808b24ad6442cb0a5a86a4f8e8e4 Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 01:13:17 +0500 Subject: [PATCH 15/24] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c1733c0..ec7183a 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ This version adds carbon trading into previously published DECO2. Direct optimisation model involves incorporating various types of emissions and prices related to carbon trading as variables, leading to the creation of -a MINLP system consisting of equations and constraints. +a MINLP formulation. Indirect optimization model takes emission rights prices as parameters, leading to less non-linearity when compared to direct optimization model. From e95150130e1d812210791d67cdf5a54b685d04d1 Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 01:13:42 +0500 Subject: [PATCH 16/24] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ec7183a..c6e5089 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ This version adds carbon trading into previously published DECO2. Direct optimisation model involves incorporating various types of emissions and prices related to carbon trading as variables, leading to the creation of -a MINLP formulation. +an MINLP formulation. Indirect optimization model takes emission rights prices as parameters, leading to less non-linearity when compared to direct optimization model. From 79017bfee350af7261f77609afb50e3b008ec96e Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 01:44:32 +0500 Subject: [PATCH 17/24] Update README.md --- Direct Optimization Model/README.md | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/Direct Optimization Model/README.md b/Direct Optimization Model/README.md index 9cbcc68..fa9e260 100644 --- a/Direct Optimization Model/README.md +++ b/Direct Optimization Model/README.md @@ -1,7 +1,3 @@ -# DECO2 - -DECarbonisation Option Optimisation (DECO2) is an optimal decarbonisation framework developed as part of a British Council-funded Trilateral Research Initiative between University of Surrey, University of Nottingham Malaysia, and University of Tokyo, with project parters De La Sall University (Philippines), Monash University Malaysia, Malaysian Green Technology Centre and Aria Sustainability. - -The framework combines rigorous mathematical programming models (mixed-integer linear programming) to determine optimal pathways to decarbonisation through implementation of carbon apture and utilisation, negative emissions technologies, and alternative energy sources for optimal energy transitions. Please read the user manual in the main folder for information on the formulation and how the inputs work. For details on the partnership, please visit our site: - -https://www.surrey.ac.uk/research-projects/software-framework-optimal-decarbonisation-planning-asean-countries +Direct Optimisation Model is developed as a standalone extension for DECO2 software. +Carbon trading model, having emission caps and prices as variables, is integrated into initial version of DECO2. +Resulting fomulation is MINLP, and it leads to greater decarbonisation and reduced cost. From ed23c006978586cb8ac3948d0766f9f1219e3e4e Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 01:47:54 +0500 Subject: [PATCH 18/24] Update README.md --- Indirect Optimization Model/README.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/Indirect Optimization Model/README.md b/Indirect Optimization Model/README.md index 9cbcc68..c564fa9 100644 --- a/Indirect Optimization Model/README.md +++ b/Indirect Optimization Model/README.md @@ -1,7 +1,5 @@ -# DECO2 +# DECO2: Indirect Optimisation Model -DECarbonisation Option Optimisation (DECO2) is an optimal decarbonisation framework developed as part of a British Council-funded Trilateral Research Initiative between University of Surrey, University of Nottingham Malaysia, and University of Tokyo, with project parters De La Sall University (Philippines), Monash University Malaysia, Malaysian Green Technology Centre and Aria Sustainability. - -The framework combines rigorous mathematical programming models (mixed-integer linear programming) to determine optimal pathways to decarbonisation through implementation of carbon apture and utilisation, negative emissions technologies, and alternative energy sources for optimal energy transitions. Please read the user manual in the main folder for information on the formulation and how the inputs work. For details on the partnership, please visit our site: - -https://www.surrey.ac.uk/research-projects/software-framework-optimal-decarbonisation-planning-asean-countries +Indirect Optimisation Model is developed as a standalone extension for DECO2 software. +Carbon trading model, having emission caps as variables and emission rights prices as parameters, is integrated into initial version of DECO2. +Resulting fomulation is MINLP (however, non-linearity iss far less when compared to Direct Optimisation Model formulation), and it leads to greater decarbonisation than initial version of DECO2 and more cost (especially when emission rights pricess are less). From 94944fb3082fe9322d35db72d04843370064fb61 Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 01:49:53 +0500 Subject: [PATCH 19/24] Update README.md --- Direct Optimization Model/README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/Direct Optimization Model/README.md b/Direct Optimization Model/README.md index fa9e260..ff4618c 100644 --- a/Direct Optimization Model/README.md +++ b/Direct Optimization Model/README.md @@ -1,3 +1,6 @@ +#DECO2: Direct Optimisation Model + Direct Optimisation Model is developed as a standalone extension for DECO2 software. Carbon trading model, having emission caps and prices as variables, is integrated into initial version of DECO2. -Resulting fomulation is MINLP, and it leads to greater decarbonisation and reduced cost. +Resulting fomulation is MINLP, and it leads to greater decarbonisation and reduced cost (when compared to emissions and costs from initial DECO2 version and Indirect +Optimisation Model formulation). From c497718a0339d835eca4d11add537130d996888f Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 01:50:42 +0500 Subject: [PATCH 20/24] Update README.md --- Direct Optimization Model/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Direct Optimization Model/README.md b/Direct Optimization Model/README.md index ff4618c..3817ae3 100644 --- a/Direct Optimization Model/README.md +++ b/Direct Optimization Model/README.md @@ -1,4 +1,4 @@ -#DECO2: Direct Optimisation Model +# DECO2: Direct Optimisation Model Direct Optimisation Model is developed as a standalone extension for DECO2 software. Carbon trading model, having emission caps and prices as variables, is integrated into initial version of DECO2. From 1b9ff2cd3d6d89710990bf30402cbc2a00138fb0 Mon Sep 17 00:00:00 2001 From: Gul Hameed <99656351+gulhameed361@users.noreply.github.com> Date: Sun, 2 Apr 2023 01:53:18 +0500 Subject: [PATCH 21/24] Update README.md --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index c6e5089..5f6d556 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ -This version adds carbon trading into previously published DECO2. +# DECO2: Integration of Carbon Trading Model and other Modifications +This version adds carbon trading into previously published DECO2. Direct optimisation model involves incorporating various types of emissions and prices related to carbon trading as variables, leading to the creation of an MINLP formulation. - +Greater decarbonisation is achieved as compared to that from initial DECO2 version. Indirect optimization model takes emission rights prices as parameters, leading to less non-linearity when compared to direct optimization model. 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Update README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 5f6d556..35c1895 100644 --- a/README.md +++ b/README.md @@ -5,3 +5,7 @@ Direct optimisation model involves incorporating various types of emissions and an MINLP formulation. Greater decarbonisation is achieved as compared to that from initial DECO2 version. Indirect optimization model takes emission rights prices as parameters, leading to less non-linearity when compared to direct optimization model. + +DECO2-2023v has recently been published in SPC journal. +https://www.sciencedirect.com/science/article/pii/S2352550923001781 +

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